In the field of personal learning environment (PLE) research is focusing on the generation and provision of recommendations. Amongst others, approaches reach from decision making tools based on psycho-pedagogical principles over specialized social recommender functionality up to general community or context-aware recommendations. The variety of the solutions results from the fact that pure collaborative filtering (CF) techniques are not sufficient for PLE-based scenarios. In this paper we propose utilizing learner interaction recordings for generating PLE recommendations fitting the educational and social context of a learner. Besides pointing out how we have realized this approach as part of a research prototype, we evaluate and discuss such recommendations generated from data captured in former studies.
Accessing web-based information systems such as online encyclopedias is driven by user information needs. As soon as users satisfy their needs for certain information they live the site of such online encyclopedias. Hyperlinks comprised by an online encyclopedia play an important role on guiding users to corresponding articles which explain certain concepts within the system. Therefore, we believe that by providing high quality links within a corpus with insucient interconnections, its network connectivity is enhanced. Consequently, gaining of networked knowledge is supported. Further, users are more attracted to follow links and stay longer on such sites. In this paper, a linking framework for the most well known Austrian online encyclopedia called Austria-Forum is presented. This linking framework, enables interlinking of semantically related articles within our corpus. Moreover, it provides the possibility to evaluate the quality of linking before making expensive e orts on automatically generating links.
It is widely agreed that information retrieval (IR) systems benefit enormously from considering not only the user’s query but also contextual data.
In enterprise IR systems corporate knowledge bases and additional manually triggered information about users are normally taken to obtain such contextual data. In this paper we propose a solution for role-specific search in enterprise environments without the need of manual administration of mappings between roles and documents. We include collaboratively constructed knowledge engineering systems for computing similarity measures between user role attributes and relevant information snippets in enterprise documents.
Our approach suggestsoptimizing such enterprise search systems by a role-sensitive ranking algorithm that relates contextually-derived information needs of users to unstructured (textual) data in documents. Hence we introduce a linguistic conceptfor generatingrole describing word vectorsbased on query (search) histories and corporate knowledge base generation.
The Introduction outlines some basic ideas concerning the major areas of enterprise search, some relevant differences between web search and enterprise search. Subsequently we sketch our optimized enterprise search model.
In Chapter 2some theoretical background and Related Work is briefly discussed.Chapter 3depicts some linguistically relevant details of our proposed model. We discuss our concept of User Roles, Role Term Vectors, some approaches for Role Term Extraction andTerm Extraction incorporating knowledge bases and query histories. In Chapter4 we describe our ranking mechanism, the re-ranking strategy and the method for Role Relevance Scoring. Chapter 5 gives a conclusion of the work as well as an outlook on future work.
Our objective is to support reasoning tasks in heritage architecture with graphics enabling analysts to visualise and share their understanding of how, from a given set of information, alternative scenarios or evolution can be inferred. The paper comments on the nature of the cognitive processes in historical sciences, and on factors that need to be weighed when interpreting sets of information. Visual solutions are proposed, and illustrated on real cases in Kraków Poland. They help spotting where alternative explanations should be considered in order to avoid unjustified assumptions and certitudes on the evolution of artefacts. The contribution expects to demonstrate that reasoning on uncertainties in historical sciences can be fruitfully backed up by concepts and practices from the infovis community.
Although inverse problems are well studied in engineering, physics and computer graphics this uncommon perspective has been less investigated in many other contexts where it also offers some potential interest. Basically an inverse problem (or inverse modeling) aims to find the parameter values of a model that can have produced a given result. This paper adopts this point of view in order to invert queries. Therefore the main objective becomes to find the query criteria that might have produced a known result set. In order to structure the field of investigation, a list of various inverse queries is proposed. Out of the five identified categories, the paper focuses on the simplest one: the inverse Boolean conjunctive queries composed of n criteria each involving one attribute taking orderable values. Some theoretical issues raised by this type of inverse query are discussed in detail. Next, the paper describes a solution to integrate such inverse queries in user interfaces. Finally a prototype implementing one of the possible solutions to materialize inverse queries is presented.
Search has een and will continue to be an important tool for users who need to locate information in an ever increasing mount of resources. Not all queries have a well defined information need that can easily be described by a keyword query Exploratory search is one such type of search where the user is not necessarily proficient in the domain or does not have a clear idea of what he is looking for. In such types of search navigation is beneficial to guide the user in his quest. In this paper we present an approach to contextual navigation search, based on a hierarchical structure constructed from folksonomy tags. The tags are associated with an extended semantic representation used to guide the navigation. Five semantic navigators are intro duced, which are navigation strategies the user can benefit from. We present a prototype which has been implemented to show the applicability of the approach to the problem at hand. The preliminary results are promising and demonstrate the ability to direct the user at interesting navigational suggestions and do cuments.
System Engineering (SE) is becoming increasingly knowledge intensive. Knowledge Management is recognized as a crucial enabler for continuous process improvement in engineering projects. Particularly, capitalization and sharing, of knowledge resulting from experience feedback are valuable asset for SE companies. In this paper, we focus on the formalization of engineering experience aiming at transforming information or understanding gained by projects into explicit knowledge. A generic SE ontological framework acts as a semantic foundation for experience capitalization and reuse. This framework is operationalized with Conceptual Graphs formalism and applied to a transport system engineering use case.
Recommender systems have established mostly in e-commerce, whereas in companies or scientific institutions the recommendation of experts und possible colleagues has yet been discussed mostly theoretically. We propose a recommender system on the basis of Social Bookmarking Systems and Folksonomies, which may help to find communities of practice, where people share the same interests and support each other in their working or scientific field. The paper reports research in knowledge management and information retrieval, and therefore offers new insights and fields of studies in information science.
The similarity relation of a number of texts is important not only for congress organizers (who need to group the proposed contributions to meaningful sessions) but to everybody who wants to find certain information within a larger number of texts. Existing information retrieval methods compare texts according to their similarity. Because these methods mostly remain on the surface of the words, the resemblance is not primary a semantic one, but a stylistic and vocabulary dependent one. Based on psychological considerations we have developed an algorithm called Hofmethode, which compares the semantic ‘environment’ of key words. Using the example of the SGP congress we show in this paper how the Hofmethode can be used to help both congress organizers and participants to find the appropriate contributions.
Efficiency of semantic caching is based on reusing of already retrieved data. Reusing of already retrieved data depends on finding the containment of new queries with the older/stored queries. Advantages of semantic cache query processing (Satisfiablity and implication in database domain) have already been demonstrated in the published iterature. Description Logic (DL) has never been applied for checking relational query subsumption/containment. Sound and Complete subsumption algorithms exist for reasoning facts represented in Description Logic. DL is a formalism used to model knowledge of a domain in the form of concepts and a rich set of associations between these concepts. Reasoning on the knowledge base can be performed in order to discover implicit relations. The most important reasoning is the determination of a subsumption relation between the logical expressions. Relational queries are also a form of knowledge description. In this paper, we discuss Subsumption analysis of semantic cache query processing and its advantages in semantic cache query management by using tableau algorithms of DL. In particular, Subsumption algorithms are used to perform reasoning from the previously stored queries in the semantic cache. Query containment can be found by transforming relational queries into DL. Based upon this reasoning, a query can be divided into cache (probe) and erver (remainder) queries. This will not only contribute towards cache query processing but have a significant contribution in the cache management, too. Previously, the implication and satisfiablity techniques in database domain were handling only conjunctive queries. In our algorithms, we handle disjunctive queries, too.
Smart information retrieval systems are becoming increasingly prevalent due to the rate at which the amount of digitized raw data has increased, and continues to increase. This is especially true in the medical domain, as there is much data stored in unstructured formats which contain “hidden” information within them. By hidden, this means information that cannot ordinarily be found by performing a simple text search. To test the information retrieval systems that handle such data, a ground truth, or gold standard, is normally required in order to gain performance values according to an information need. In this paper we emphasize the lack of freely available, annotated medical data and wish to encourage the community of developers working in this area to make available whatever data they can. Also, the importance of such annotated medical data is raised, especially its importance and potential impact on teaching and training in medicine. As well as this, this paper will point out some of the advantages that access to a freely available pool of annotated medical objects would provide to several areas of medicine and informatics. The paper then discusses some of the considerations that would have to be made for any future systems developed that would provide a service to make the creating, sharing, and annotating of such data easy to perform (by using an online, web-based interface, for example). Finally, the paper discusses in detail the benefits of such a system to teaching and examining medical students.
Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits the relationships between users and recommends the items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF al-gorithms. In this paper, we propose to use clustering techniques on the social network of users to derive the recommendations. We study the application of this approach to academic venue recommendation. Our interest is to support researchers, especially young PhD students, to find the right venues or the right communities. Using the data from DBLP digital library, the evaluation shows that our clustering technique based CF performs better than the traditional CF algorithms.
knowCube®, a novel multi criteria decision making tool, is introduced. Its user-friendly interface assists “intuitive surfing through decision spaces” by means which are also familiar to non-experts. Causes and effects of alternatives may be examined from different points of view, and several types of criteria – like quantitative or qualitative, dependent or independent, hard or soft, and all mixed together – can be handled at the same time. The tool’s broad applicability is illustrated by some application examples from absolutely different fields: Mixed Sandwiches of various materials are investigated in manufacturing, ideal Foams are produced due to optimal parameter settings, and personalized Drugs could be designed by balancing conflicting effects.
The detection of redundant or reused passages in texts is an important basis for various tasks including tracking of information flow, plagiarism detection, origin detection, web search and information retrieval. Being able to track the evolution of a piece of information through different revisions or instances of documents can generally help to gain an impression of the document’s background. In this paper we propose an efficient algorithm for detection of textual overlap between documents as well as a tool for its visualisation, created in the course of the Holinshed Project at the University of Oxford. The Evaluation on an annotated corpus shows that the proposed algorithm performs better than state of the art approaches.
One of the core challenges for current landscape of ontology based research is to develop efficient ontology merging algorithms which can resolve the mismatches with no or minimum human intervention, and generate automatic global merged ontology on-the-fly to fulfil the needs of automated enterprise business applications and mediation based data warehousing. This paper presents our approach of ontology merging in context of data warehousing by mediation that aims at building analysis contexts on-the-fly. Our methodology is based on the combination of the statistical aspect represented by the hierarchical clustering technique and the inference mechanism. It generates the global ontology automatically by four steps. First, it builds classes of equivalent entities of different categories (concepts, properties, instances) by applying a hierarchical clustering algorithm. Secondly, it makes inference on detected classes to find new axioms, and solves synonymy and homonymy conflicts. This step also consists of generating sets of concept pairs from ontology hierarchies, such as the first component subsumes the second one. Third, it merges different sets together, and uses classes of synonyms and sets of concept pairs to solve semantic conflicts in the global set of concept pairs. Finally, it transforms this set to a new hierarchy, which represents the global ontology.
Semantic web provides virtual communities that enable intelligent interaction between software agents and people due to availability of standard open ontologies. But, as the semantic web is gaining much popularity, there is a massive growth seen in the ontology development which poses new research challenges such as ontology classification, ranking, searching, retrieval, etc. This results many recent developments, like OntoKhoj, Swoogle, OntoSearch, that facilitate user for such tasks. These semantic web portals mainly treat ontologies as plain texts and use traditional classification algorithms of plain text for classifying ontologies in directories and assigning predefined labels rather than using semantic knowledge hidden within the ontologies. These approaches suffer with many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define ontology classification problem and categorized it into many sub-problems. We present a new methodology for ontology classification that is based on ontology approach for ontology classification and retrieval. The proposed framework, ONTCLASSIFIRE, benefit construction, maintenance or expansion of ontologies directories on the semantic web, and helps in ontology management and retrieval for software agents and people. We conclude that the use of context specific knowledge hidden in ontologies gives more accurate results of ontology classification and retrieval.
Knowledge sharing between individuals has traditionally been conducted using faceto- face conversation. In the networked society – initially formed by telegraphs and the phone and nowadays powered by the Internet – many acts of knowledge sharing are carried out in a mediated fashion. While this typically introduces a number of problems in the knowledge sharing process, it also offers certain advantages. In this paper, we describe a framework for analyzing different modes of knowledge sharing. Furthermore, we line out the concept of “need-driven” knowledge sharing to address limitations in current mediated knowledge sharing approaches.
With the accelerated growth of the blogosphere, automatically analyzing blogs (specifically extracting information) becomes increasingly important. Here, we focus on the fundamental task of automatically detecting blog topics in order to support users to explore a collection of blogs by focusing on different particular topics according to their interests. We show that topic exploration can be significantly improved (by up to 33%) by using a novel approach to
This paper proposes an approach for automatically extracting symptoms associated to a given disease from semantic-based descriptions of health records of patients affected by an investigated pathology. The proposal implements non-standard reasoning services developed in Description Logics for the individuation of informative commonalities in concept collections and can make significantly easier the diagnosis process of rare and unknown diseases.
Common graph visualizations tend to produce edge crossings and overlaps when used to display resource collections that are highly interrelated via shared properties. This hampers visual exploration and understanding of relationships between resources and can negatively affect information and knowledge management. In this paper, we present a new approach that visualizes resources and their shared properties in chains to prevent dense graphs and to better support the exploration of relationships. We explain the basic idea, describe an appropriate algorithm and discuss optimization issues. Furthermore, we report on a comparative evaluation showing that this kind of graph visualization supports particularly the visual tracking of relationships and the identification of commonalities between resources.
There are plenty of projects aimed at incorporating semantic information into present day document processing. The main problem is their real-world usability. E-learning is one of the areas which can take advantage of the semantically described documents. In this paper we would like to introduce a framework of cooperating tools which can help extract, store, visualize semantics in this area.
We introduce a framework for a new idea tool Note, which gathers, fosters and manages innovative ideas. Note supports the development of organizational memory and is connected to the practices of organizational innovativeness. The tool utilizes text mining methods in idea processing, management and visualization and is thus a new approach in idea management software. The tool is under development.
Thematic portals are sites where subject matter experts select and organize information and, consequently, they can be described as top-bottom structures. While challenging this schema, implementing social information retrieval systems and social network representation features can improve user experience and the dissemination impact of the portal. We analyse the characteristics of social software and review examples of utilisation of these technologies that can be applied in an educational technology thematic portal.
Recently, the information community has seen the emergence of user-centric media applications, which are characterized by the central position given to the user. To fulfill the user-centric promise, it is necessary to understand and model the actions of the users of the system. This position paper presents a methodology for modeling the behavior of multimedia database users. To this end, we propose to analyze the query logs to derive the classes of behaviors of a user. The presented method bases on the characteristics of user queries and on taxonomies. The behaviors are established using a query classification algorithm.
Finding expertise is an important task required in all organisations and institutions. In looking for expertise, one typically relies on the compilation of information from multiple sources such as organisational directories and social networks. This approach has been applied to enhance the Journal of Universal Computer Science to enable it to become a still more valuable scholarly resource. This paper describes a multi-faceted representation of expertise, by consolidating human specified expert profile with systemic assessment of expertise. The multifaceted approach is an important in the consolidation of information from multiple sources, in an effort to expand on the characterisation of expertise. The strength of this approach is drawn from the incorporation of intangible metrics for expertise assessment. This paper has revealed interesting directions for the automatic discovery of expertise in scholarly communities.
The quality of the current tagging services can be greatly improved if the service is able to cluster tags by their meaning. Tag clouds clustered by higher level topics enable the users to explore their tag space, which is especially needed when tag clouds become large. We demonstrate TagCluster – a tool for automated tag clustering that harnesses knowledge from Wikipedia about semantic relatedness between tags and names of categories to achieve smart clustering. Our approach shows much better quality of clusters compared to the existing techniques that rely on tag co-occurrence analysis in the tagging service.
Social media sharing sites such as Flickr or YouTube have become immensely popular. Besides sharing actual content, users also share annotations describing or classifying the contents they publish. Although tagging is easy, annotation still is a laborious task that can be made easier by suggesting meaningful additional tags to the user automatically. In this position paper we propose a system architecture and process for supporting annotation by tag suggestion to increase the quality and quantity of social annotations. The goal is not to tag previously untagged images in a completely automatic way, but instead to extend the amount and completeness of annotations by supporting the user in the process of adding further tags.
Many enterprises loose work time because they lack of global search solutions or their solutions are not able to satisfy the needs in a reasonable time. This results in costs for lost work time as well as increased response time. We present a novel approach to federated search engines that use case based reasoning to rerank results according to the searchers needs and therefore leads to a higher quality of search results and faster information retrieval.
When studying heritage artefacts, and trying to represent what we know of them, it is important to portray not only key moments in their evolution, but also processes of transformation. In this contribution, we introduce a methodological framework of description of architectural changes, and investigate diagrammatic representations as means to visualize the above mentioned framework. We introduce two types of diagrams (diachrograms that distribute along a time axis transitions and states, variograms that detail the nature of the changes) that should help better understanding, how changes over time affect architecture. The paper also underlines key aspects of data in “historical sciences”: uncertainties, incompleteness, long ranges of time, unevenly distributed physical and temporal stratifications.
Companies are faced with managing as well as integrating large collections of distributed data today. Here, the challenging task is not to store these volumes of structured and interlinked data but to understand and analyze its explicit or implicit relationships. However, up to date there is virtually no support in navigating, visualizing or even analyzing structured data sets of this size appropriately. This paper describes novel rendering techniques enabling a new level of visual analytics combined with interactive exploration principles. The underlying visualization rationale is driven by the principle of providing detail information with respect to qualitative as well as quantitative aspects on user demand while offering an overview at any time. By means of our prototypical implementation and a real-world data set we show how to answer several data specific tasks by interactive visual exploration.
An increasing amount of valuable information is stored in RDF. In order to let humans access this information, providing an appropriate visualization of RDF data is an important challenge. In this paper, we present a new approach, combining list and a graph visualization to counterbalance the respective disadvantages of both representation paradigms to better handle the complexity of both the size and the
structure of RDF data.
The stories told among members of an organization are an effective instrument for knowledge socialization, the sharing of experiences through social mechanisms. However, the utility of stories for organizational learning is limited due to the difficulties in acquiring stories that are relevant to the practices of an organization, identifying the learning goals that these stories serve, and delivering these stories to the right people and the right time in a manner that best facilitates learning. In this paper we outline a vision for story-based organizational learning in the future, and describe three areas where intelligent technologies can be applied to automate story management practices in support of organizational learning. First, we describe automated story capture technologies that identify narratives of people’s experiences within the context of a larger discourse. Second, we describe automated retrieval technologies that identify stories that are relevant to specific educational needs. Third, we describe how stories can be transformed into effective story-based learning environments with minimal development costs.
Determining fields of excellence in the know-how of knowledge intensive companies is often a crucial decisional process, aimed e.g., at identifying the competence to be strengthened or to invest on in a long term strategy. In this paper we propose a semantic-based approach for automatic extraction of such a specializing knowledge, usually called Core Competence in knowledge management literature. The proposed approach exploits Description Logics as formalism for the representation of knowledge sources and implements novel reasoning services, in particular informative common subsumers specifically devised for Core Competence evaluation.
In today’s information environments, tagging is widely used to provide informationabout arbitrary types of digital resources. This information is created by end users with different motivations and for different kinds of purposes. When aiming to support users in the tagging process, these differences play an important role. This paper discusses several approaches to generate tag recommendations, and a prototypical recommender system for the social resource sharing platform ALOE will be presented.
This interactive system allows users to control the generation of the recommendations by selecting the sources to be used as well as their impact. The component was introduced at DFKI, and a first evaluation showed that the recommender component was considered as helpful by a majority of users.
Story-telling has been a means of bequeathing knowledge since aeons. Whereas the idea of the story-telling remains unaltered, the world around undertakes continuous changes. New media, new technologies and devices, new ways of communication define a new format of the story-telling. This paper proposes a new non-linear version of mobile story-telling in the emerging ubiquity of knowledge sharing. To support our concept we provide a description of multimedia services based on the MPEG-7 metadata standard used for our non-linear mobile story-telling environment.
A user-centric architecture of infotainment content adaptation to the context is presented. The architecture uses component technologies in term of business logic and functionalities offered by social web (OpenID, FOAF) and semantic descriptions of MPEG-7 and MPEG-21. Technological alternatives are discussed and adapted to the specificity of vehicle applications in terms of scalability and platform mobility. The requirements of the architecture are motivated by the presentation of a scenario.
Web 2.0 platforms such as media sharing and social network sites (SNS) concern people in everyday life to a great extent. People are enabled to reach out to various media and up to now, it is nearly impossible to use digital identities ex ante or to recreate users’ identities ex post across different platforms. In this paper, we explore important methodologies in Web 2.0 such as cross-media analysis and social pattern based analysis based on a survey in this area, aiming at cross-platform information diffusion across social network sites. Open issues are discussed to explore the challenges and solutions in this new research area.
Wikis have their seeds in the easy collaborative editing and maintenance of web pages. This was picked up by tremendously successful public projects such as the online encyclopedia Wikipedia. Creating, modifying and maintaining of wiki articles implies social structures and dependencies between wiki authors and wiki articles themselves. The general challenge of this work is to consider these structures as dynamic evolving networks and to point out prominent behaviors in large wiki-based networks. We present an environment capable of handling data management, measurement and visualization issues for the dynamic network analysis of publicly available wiki data.
Developing successful knowledge management (KM) processes is extremely difficult. In general, a large number of all KM projects end unsuccessfully. The aim of this paper is to summarize and study the attempts to take advantage of Lessons Learned in the Swedish Armed Forces (SwAF), focusing on international missions. Relevant reports, articles and literature have been studied. With the purpose of understanding the reasons for failure and the failure factors in SwAF’s attempts at KM, Chua and Lams’ model for unsuccessful KM implementation has been applied to four cases from the organization. The results show that SwAF are aware of the importance of knowledge and have attempted to implement KM on several occasions. In most cases, however, the KM projects do not achieve widespread use and eventually end unsuccessfully. Furthermore, many of the KM tools that have been developed are no longer in use. The Swedish Explosive Ordnance Disposal and Demining Centre (SWEDEC) and the Swedish Air Force are notable exceptions.
Tagging is an extremely popular mechanism in many Web 2.0 applications to create metadata supporting search and retrieval of arbitrary multimedia information like digital images, video or audio. However, compared to the syndicated multimedia information itself, the metadata are still “sticky”. They cannot be accessed across several Web 2.0 applications, their semantic enrichment is not possible and they cannot be embedded in the local practices of communities of practice. Here, we present a multimedia tagging mechanism based on the international standard MPEG-7 for community-aware, standard compliant tagging of semantically enriched metadata implemented in the M7MT proof-of-concept application.
We present the Publication Platform, a component of the PrestoSpace1 project, which provides retrieval and browsing functionalities of enriched audio-visual material. The Prestospace Factory is a system for enriching audio-visual documents in order to provide automated content and semantic analysis.The Publication Platform provides a user interface for semantic queries and produces a Web page with the results of the AV analysis and additional information about related external documents.
The task of creating specific references rests on specifications that qualify how parts of resources can be addressed. The lack of standards for fragment identifiers has lead to the problem that links, metadata and references merely point to whole resources. Although it is suggested that fragment identification is specified with a media type’s MIME type registration, there are few formats that provide a fragment identification scheme. Furthermore formats that specify fragment identification schemes have not agreed on a common set of requirements. In this paper we present an overview of the current status of interoperable fragment definitions, point out promising activities that promote interoperable fragment definitions and suggest strategies to promote uniform fragment identifiers. Additionally a set of requirements is defined and described to ease the development of fragment identification standards.
We present an innovative approach that treats the right management metadata as metric objects, enabling similarity search on IPR attributes between digital items. We show how the content base similarity search can help both the user to deal with a huge amount of similar items with different licenses and the content providers to detect fake copies or illegal uses. Our aim is the management of the metadata related to the Digital Rights in centralized systems or networks with indexing capabilities for both text and similarity searches, providing the basic infrastructure enabling the private use and the commercial exploitation as well.
Due to a shift in the marketing focus from mass to micro markets, the importance of one-to-one communication in advertising has increased. Interactive media provide possible answers to this shift. However, missing standards in payment models for interactive media are a hurdle in the further development. The paper reviews interactive advertising payment models. Furthermore, it adapts the popular FCB grid as a tool for both advertisers and publishers or broadcasters to examine effective interactive payment models.
Allowing users to publish and share photos on the Internet makes Flickr one of the most popular tagging services currently available. The organisation of images in Flickr is based on Folksonomies, where users attach loose metadata—instead of well-defined terms from a controlled vocabulary—to their images. Although this lowers the barrier to participation it has a number of negative effects and can make searching, for instance, more difficult.
This paper offers a solution to a particular issue that can be encountered in Flickr—the Vocabulary Problem. The suggested approach is based on the use of a semantic lexical database for expanding Flickr queries. WordFlickr, a prototype implementation of this concept, is presented together with FlickrClustr, a related tool for clustering Flickr search results. Results of informal tests with these tools are provided, and characteristics of tag usage are derived.
With the advent of Web 2.0 technologies a new attitude towards processing contents in the Internet has emerged. Nowadays it is a lot easier to create, share and retrieve multimedia contents on the Web. However, with the increasing amount in contents the retrieval process becomes more complex and often leads to inadequate search results. One main reason is summarized easily: Approaches to image clustering and retrieval usually either stick solely to the images’ low-level features or their semantic tags. However, this is frequently inappropriate since the “real” semantics of an image can only be derived from the combination of low-level features and semantic tags. Consequently, we investigated a more holistic view on semantics based on a system called Imagesemantics that tries to close the gap between both approaches by combining them.
Classical knowledge representation methods traditionally work with established relations such as synonymy, hierarchy and unspecified associations. Recent developments like ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships. In a summarizing overview we show which relations are currently utilized in elaborated knowledge representation methods and which may be inherently hidden in folksonomies and ontologies.
Analysing and understanding the evolution of historic artefacts requires the crossexamination of indications ranging from specific pieces of data (remains of the edifice, archival materials, etc.), to generic pieces of knowledge (historical context, comparable cases, theory of architecture, etc.). This research is based on the premise that the artefact’s acts as a media allowing the integration of the above-mentioned heterogeneous indications. Consequently, they may enable information visualisation and retrieval through 2D/3D dynamic graphics. In this contribution, we discuss four SVG-based graphic tools aiming at exploiting visually the relations between an artefact and the above-mentioned indications, i.e. its documentation.
For investment decisions the development of market trends is very important. In this contribution we present our results concerning the influence of news on market trends. We processed the stock news delivered by the Wall Street Journal with two methods of text mining – Bayes classification and grammar-driven classification. We found some potentialities of Dow Jones trend prediction and present promising results.
Self-Organising Maps are utilised in many data mining and knowledge management applications. Although various visualisations have been proposed for SOM, these techniques lack in distinguishing between the items mapped to the same unit. Here we present a novel technique for the visualisation of Self-Organising Maps that displays inputs not in the centre of the map units, but shifts them towards the closest neighbours, the degree of the movement depending on the similarity to the neighbours. The night-sky visualisation facilitates better understanding of the underlying data. We report results from applying our method on two synthetic and a real-life data set.
In this paper, we present GlobeMash, a mashup web application that uses standardized data formats like XML, JSON, LOM, SVG, CAM and protocols like SOAP, HTTP, to enable users to access the repositories of the Global Learning Objects Brokered Exchange (GLOBE) consortium. GlobeMash uses the Google Maps API to display the repositories and results on a geographical map, the Timeline API of the SIMILE project and the federated search layer of the GLOBE infrastructure. It enables users to query all the repositories in GLOBE and to get an insight in their search history by visualizing the latter as a combination of an extended tag-cloud and a synchronized timeline.
In this paper, we introduce a so-called DyVT tool (Dynamic social network Visualization Tool) to support spatiotemporal knowledge visualization and discovery in dynamic social networks. The dynamic aspects of social networks refer to contextualized information such as spatial, temporal as well as users’ personalized information. We also define an XML-based target language incorporating emerging formats like DyNetML, KML, and GraphML. It also provides means to express, store and interchange the dynamic aspects of complex dynamic social network data. Based on this language, users can animate and personalize spatiotemporal knowledge extracted from social network data like email threads or blogs. In addition, a Java based graphical user interface is also available to enable nonexperienced users to customize knowledge visualization easily. A mashup with Google maps for spatiotemporal visualization is provided. With this tool spatiotemporal knowledge on an IBM DB2 Mailing list database containing 69 mailing lists and 56389 mails altogether is well explored.
The requirements on learning support from knowledge work differ compared to traditional work. Based on those observations an approach for supporting learning in knowledge work is proposed considering requirements from e-learning as well as from knowledge management. In addition to traditional e-learning, on-demand e-learning takes the current situation of the knowledge worker into consideration to ensure learning support of knowledge work is proposed. For using a broad variety of resources in on demand e-learning a single metadata schema for describing seems not sufficient for every organisation. Therefore, application profiles appear adequate for describing resources used in the proposed approach for arranging knowledge elements. Identifying the knowledge workers current situation a learning need should be derived and to use it afterwards for selecting and delivering knowledge elements.
The evolution of our society to the knowledge based society has raised new challenges for most of the scientific domains that exist. The higher importance given to knowledge extraction instead of getting just information (i.e. data included in a context) hast led to the development of several intelligent techniques for knowledge discovery. This paper shows some examples of using the techniques of case-based reasoning and data-mining for knowledge discovery in the knowledge management system of an university. We have taken as example, the educational domain with the particular case of universities as they represent good examples of organizations that acquire, generate, store and use knowledge for various purposes, teaching, learning and research.
The paper starts with the wish list for a “perfect” information exploration tool, where the topics of that list are collected from the work of some pioneers and experts in this field, as e.g. from C. Ahlberg, C. Williamson and B. Shneiderman. After that, a novel multi criteria knowledge management technique is introduced, that comes fairly close to the wish list given. This will be demonstrated by looking on some general aspects of information exploration, and how knowCube, a user-friendly software tool supporting graphical decision making, masters such tasks, where user interaction happens via standard drawing tools, like pen, brush or text marker. The paper ends with an outlook on FilmFinder – Version knowCube.
The innovation process is a rhythm of search and selection, exploration and exploitation, cycles of perspectives encountering which allow people to analyze problems from new points of view. In order to enable innovation, a lot of instruments have been developed connecting heterogeneous individuals thinking (e.g. social networks, web portals, wiki systems, organizational yellow-pages, etc.). In this paper we focus on web portals, and how these tools assist the users connections and the innovation processes among them. In particular, we analyze some services implemented in the Innovation Portal of the Brazilian Ministry of Science and Technology geared to stimulating the establishment of strategic partnerships and cooperation projects involving national firms and science institutions. These services are mainly based on social network analysis in order to manage connections (i.e. coordination) and innovation processes among users.
In many respects Web 2.0 and Knowledge Management (KM) are strongly related to each other. From a KM perspective the Web 2.0 evolution can serve as a pool of ideas for new ways of knowledge sharing, knowledge organisation and for the development of new architectures of measurable knowledge management systems. KM projects are usually developed in a process-oriented, goal-driven environment, embedded in complex organisational structures, whereas typical Web 2.0 applications like del.icio.us, flickr or friendster are building “their own context”. This paper will examine Tim O´Reilly’s eight generic Web 2.0 design patterns in terms of their applicability for a measurable KM System in an Enterprise 2.0. Two use cases will be presented and it will be discussed which of the design patterns could be enriched by technologies from the semantic web which will be summarized as a concept named “Enterprise n+1”.
This paper discusses the requirements of a framework for sharing digital resources and metadata to meet the needs of open, flexible Knowledge Management solutions. The changing nature of the Web and its users as observed in recent years clearly establishes the need for new approaches and technologies to fully exploit the potential for working with existing digital resources. Formal metadata about the resources can be combined with information created in lightweight and user-centric approachesin order to significantly enhance resource descriptions and enable more efficient access to existing knowledge. The ALOE system, currently in development at DFKI, is one such solution and it is used here as the basis for a sample realization of an appropriate framework.
Process management is an important task in many companies. However, most of the literature on process management focuses on aspects like execution and monitoring and does neither deal with knowledge management support for the maintenance and contextualization of process models nor with the integration of such tasks into work procedures and corresponding tools. An effective knowledge management for business processes needs meta-data describing not only the processes but also their parts and details. This paper proposes a knowledge infrastructure for process modelling, usage and maintenance, which is based on a social tagging approach derived from popular social bookmarking tools. The concept of a tag-based prototype is described, which enables knowledge management support for complex sets of processes.
Abstract: In many companies search solutions span departments and functional operations, independent of individual working processes in particular departments. The efficient access to the required information in relevant sub-processes is a key factor for the performance of the enterprise. Optimizing the searching and finding of necessary information enables a better workflow, and thus reduces costs. Based on well known process modelling methods we present a guideline and methods to analyse and document relevant characteristics of information intensive processes. In a next step these observations will serve as a basis for a mapping between identified requirements and suitable methods and tools for an optimal support of the process.
The success of multimedia enabled community engines depends on a careful design of the digital media and the related communication/collaboration tools. However, the semantics of the multimedia contents in community communication and collaboration is hard to capture and complex to compute. With the opportunities given by the combination of metadata descriptions standards like MPEG-7 and server-side content-based computations, the manageability of multimedia semantics in community engines becomes more feasible. As a proof of concept, we introduce a Lightweight Application Server for MPEG-7 Services (LAS) which is deployed for a web-based high-level semantic annotation tool for arbitrary images.
The POLYMNIA project aims to develop an intelligent cross-media platform for personalized leisure and entertainment in theme parks or recreation venues. The visitors of the theme park are – on request – identified, tracked and recorded individually in order to create personalised photos and videos documenting the visit. One specific problem in this system is the identification of humans across different cameras and under varying environmental conditions. We use the MPEG-7 Colour Structure Descriptor (CSD) for this purpose which has been reported to perform well for this application. We propose a new distance function for the CSD, the weighted city block distance. Evaluation shows that the new matching function yields better results than the distance proposed in the standard.
European mobility initiatives encourage students to study abroad, but experiences of intercultural and professional learning during the stay abroad do not flow back and the direct exchange between these students is not supported. In this article we want to present our concept and experiences with the tool weblog supporting students of Social Work during their placements abroad.
When facing partial evidence on how architectural objects evolved through time (often due to uncompleted information), it is important to provide the researcher with tools for a cross-examination of cases that may help him better delineate possible values for lacking information. In the case of architecture, we deal with data that can be attached to a given location (distribution in time and space) and to a given generic typology. This opens an opportunity to use pseudo cartographic representations in order to visually distribute objects that share a common typology. Unlike with geographical maps, we should however include visual signs that tell the user about the architectural composition of each object in the data set, as well as about its level of documentation. In this paper we try to demonstrate, using a data set concerning antique theatres, that visual comparative evaluations of the data can provide a major
methodological breakthrough for cross-examination of information on architectural objects.
In this paper we discuss an interactive visualization application that aims to visualize a large repository of small reusable content components that were created by disaggregating legacy content. The purpose of this decomposition is to produce content that can be automatically reused in on-the-fly assemblies of new learning objects. The purpose of the visualization application is to offer insight in the structure of the contents of the repository and to enable access to them in an effective and efficient way.
Daily work with information spread across multiple data sources is still a time consuming task when it comes to managing, searching and securely distributing to dedicated recipients. The paper describes the generation of a homogeneous knowledge representation extracted from heterogeneous personal data sources. Used for unified navigation through personal knowledge it assists the user in retrieving any information even with limited devices such as smartphones through a single interface.
Information Realisation is the process of presenting data as Textual, Graphical or Audial information to a human user. In this paper, we discuss the importance of this concept with respect to the accessibility of Semantic Web data to a diverse target audience. We provide an ontological point of view, defining the expressive characteristics and application domain of representation formats, thus presenting a system which produces representations customised to the user environment and the nature of the source data. Our approach considers the semantics of the data, not just the structure, and aims to present the information in the most semantically appropriate manner for the given target environment. We provide examples of a simple data set being realised as popular target representation formats: textual (XHTML, RSS); graphical (SVG, X3D); and audial (SoundML, VoiceXML).
Digital media are used to facilitate social structures thus building digital social networks. Disturbances in such networks occur on different levels (egocentric level, subgroup level, network) and have to be analyzed in the multidimensional context of reference disciplines like sociology and knowledge management. This paper presents a first repository of disturbance patterns for the analysis of digital social networks. Based on the Actor-Network Theory and the Social Network Analysis, new socio-theoretical models for handling complex media settings were developed. On these models a pattern language is defined to describe multidimensional disturbance patterns and to store them in a newly developed pattern repository. The core of the pattern language is the formal expression language for pattern (FELP) which used to specify the structural and the content-specific properties of digital social networks. Results can be visualized with open source graph visualization software. To evaluate the approach a case study has been performed in a repository containing 118 mailing lists and 17.359 individuals. Patterns like troll, spammer and burst have been applied successfully.
This paper contributes to a facet from the area of Web Information Retrieval that has recently received much attention: The satisfaction of a user’s personal information need with respect to text type, presentation type, or information quality. We imply that such properties can be quantified for all kinds ofWeb documents, and we subsume them under the term “Web genre” or “genre”.
Recent surveys show that there is, to a certain degree, a common understanding of Web genre. However, the strictness by which genre and non-genre aspects of a document are experienced is an individual matter. To get a better understanding of the challenges of Web genre identification and its possible limits we investigate in this paper a very interesting question, which has not been posed by now: Given a categorization C of documents (or bookmarks, links, document identifiers), can we provide a reliable assessment whether C is governed by topic or by genre considerations? We present instruments to answer this question as well as to make a distinct statement about the homogeneity of a categorization.
Being confronted with rising requirements by automobile manufacturers, for enterprises from automotive supplier industry efficient support of knowledge-intensive core business processes along the product life cycle (PLC) gains in importance. Since in a product’s development phase its cost is mainly determined, efficient (re-)use of knowledge about a product and it’s manufacturing process is crucial. In this paper an approach is presented to support the knowledge-intensive business process “offer engineering”. Support is provided by analyzing product- and process-related data to find parts similar to a newly requested product. A tool is presented which allows a search process with arbitrary attributes. First results validate the usefulness of this approach for conceptual planners in automotive industry.
Effectively managing organizational knowledge is a key in today’s knowledge intensive businesses to evolve a company’s future development. This essential task is supported by software systems, providing means to share, structure and work on knowledge items. In a case study at the Innovation Center Hamburg of Philips Semiconductors, different views on organizational knowledge determined by functional working contexts are elaborated. Considering these contexts, providing dynamic but also concise and recognizable structures to knowledge items as well as enabling cooperative work on them describe the main challenges of a supporting infrastructure. As a promising concept, the metaphor of virtual knowledge rooms is introduced. Based on a technical framework supporting this metaphor natively, a knowledge management system was developed, meeting all requirements and providing an extremely flexible and easy to maintain solution to the initial problem.
Today, most computer users work with traditional hierarchical file systems for organizing large amounts of personal files. Recently, tagging has grown popular as
an alternative means of organizing information resources. We argue that tagging is a powerful paradigm for efficient information access which overcomes many deficiencies of hierarchical file systems, especially in the context of the organization of large quantities of personal files. In this paper we present TagFS, a filesystem with tagging support which aims at a seamless integration of the tagging paradigm with local applications. While retaining the notions of directories and files and providing all standard filesystem operations, the semantics of these primitives are changed to modifications of the tag annotations.
Because of the costly production of Learning Resources the Re-Use of existing Learning Resources becomes more and more important. But reusing Learning Resources in a new context makes it necessary to adapt them. We have developed a framework as base of a Re-Purposing Tool to support users to perform adaptations of Learning Resources in different dimensions which are crucial when using existing Learning Resources in a new context. The adaptation of Learning Resources is not easy to perform and comprises challenges like to deal with multiple files in multiple formats. To hide this from the user there is need for an abstraction of the underlying details. With a model which is including only the information the user needs and which is abstracting from the obstacles an adaptation of Learning Resources becomes a possible task even for novice users. This paper points out the content representation which is used in the framework to abstract from the given Learning Resources as well as the content ontology which the content representation is based on.
This paper describes an ontology supported software prototype that combines the advantages of existing Internet search engines with modern text analysis functionalities and an intelligent storage system for documents and knowledge items. The ontology assists the user in query definition and structures the storage of documents as well as knowledge items. The system is implemented and tested for the business case of SMEs that want to internationalise. It can easily be transferred to other domains just by changing the ontology.
Many IT-based Knowledge Management Systems rely on the use of conceptual structures of various kinds, such as Topic Maps or Ontologies to organise and guide the information supply in knowledge intensive tasks. Generally, these structures try to implement some semantics on top of the mostly textual information sources. The appropriateness of such structures is crucial for the success of KM-Systems of this type, but currently no measures exist to estimate how well a conceptual structure fits to the needs of the user or how well it covers the key concepts in the information sources. This paper introduces the general concepts of coherence and coupling of conceptual structures that should provide indicators on the suitability of such structures to the user and the information sources respectively.
Knowledge sharing, exchange and communication are critical tasks in any knowledge management or e-commerce initiative. In order to solve these tasks ontologies can be used. But there are certain communication problems with ontologybased knowledge sharing and exchange connected with context. The paper describes these communication problems, defines two contexts types and suggests methodical basis of solution for communication problems. This methodical basis is considered and factored in the following case study using consecutive mapping between different of context types and content ontology. This case study describes Knowledge Navigator – a map that relates contents of Formalized Management methodology with the corresponding context in order to reach effective knowledge communication to end users.
The htmlButler project was started 2005/2 and aims at enhancing the usability of visual wrapper technology without restraining flexibility. htmlButler will allow an untrained user, to visually specify simple and, a more tech-savvy user, more complex wrappers. What is new in htmlButler is that (a) the application is entirely server based, the user accessing it through a standard browser, (b) because of the centralization the knowledge of already created wrappers can be reused, and (c) users can contribute narrow and precise semantic concepts that help the system in recognizing potential meaning in web pages, thereby alleviating the complexity of future wrapper configurations
Investigating the evolution of patrimonial architecture requires gathering and analysing a mass of documentary sources, the interpretation of which may authorise researchers to produce graphical simulations of the morphological evolutions of edifices. We have demonstrated that such representations can be used as graphic interfaces in which architectural objects are located in time and space. However in the field of the architectural heritage, due to the lack or incompleteness of the documentation, at the beginning of an investigation objects are often known to researchers only by their toponimical reference: their contextual names. In the early phases of an investigation process, it is most often impossible to state with any reasonable accuracy what morphology an object had. Moreover, other clues to the understanding of the edifice and its evolution, such as terminology/ontology or analogies, can be gathered by the researchers before someone actually states what morphology the object could have. Aiming at improving the comprehension of the complex and discontinuous process of knowledge acquisition we introduce a generic formalism of information integration that lets the researchers to gather indications little by little, and allows them to follow up visually the knowledge acquisition process. This paper introduces the use of toponymy as a start point for the analysis of the edifice, and
describes the formalism we have developed in order to generalise this approach.
The successful and efficient querying of information in electronic information pools is becoming increasingly important in today’s information society. At the same time the quantity of existing information is continually growing. Querying scientific literature and selecting relevant hits are typical examples for this. [Börner and Chen 02] present possibilities to display structured information pools visually in order to simplify querying and selection processes. However, current interaction possibilities are limited to the manipulation of hit images (cutting out, zooming, rotating). The structure features of the underlying information pool remain unconsidered. Our JADE interface uses this structure information additionally to support the refinement task as well as the navigation within the space of query hits. It is based on mathematical procedures known as formal concept analysis. We carried out an evaluation study in order to determine the efficiency of the interactive visualisation techniques provided by JADE. Psychology students were made to perform various query tasks with a literature database. One group worked with a common web interface. The other group worked with JADE. The query results articulate a clear advantage for utilising interactive visualisation techniques in regard to the common parameters of query tasks, precision and recall.
Although both e-learning and knowledge management are about facilitating learning in organization, the major obstacle to bring both of them together can be traced back to different paradigms of learning, resulting from the different nature of the knowledge they are dealing with. In this paper, a knowledge maturing process is presented to illustrate the change of nature and the discontinuities. This lays the foundation for a better understanding. In order to overcome the discontinuities, the consideration of context is proposed, which offers the required continuity.
Knowledge management approaches for weakly-structured, ad-hoc knowledge work processes need to be lightweight, i.e., they cannot rely on high upfront modeling effort. This paper presents a novel prototype for supporting weakly-structured processes by integrating a standard to-do list application with a state-of-the-art document classification system. The resulting system allows for a task-oriented view on an office worker’s personal knowledge space in order to realize a proactive and contextsensitive information support during her daily, knowledge-intensive tasks.
Process oriented knowledge management focuses on knowledge intensive business processes. For modelling and analysis of these processes the modelling technique KMDL (Knowledge Modeling and Description Language) has been developed. KMDL is a method to describe knowledge flows and conversions along and between business processes. Thereby KMDL identifies existing and utilized information as well as knowledge of individual participants and of the entire company. This research-in-progress contribution introduces a practical example in the field of software engineering, in which KMDL models are evaluated to identify process improvements, e.g. by adding knowledge management activities. Therefore three individual views focussing on selected aspects of interest are introduced.
Knowledge sharing in communities has attracted much attention in the field of knowledge management in research and practice. In this paper we outline a view where the community lifecycle is supported at different stages. The central component of our framework is the community ontology SWRC+COIN that describes the typical structure of communities. We exemplarily show how communities in the academic domain can be detected automatically by means of analyzing information flow in a bibliographic Peer-to-Peer system and how the instantiated community knowledge base can be exploited to support cooperative work in the communities.
In the paper, the emerging mobile knowledge portals are analysed both from the technological and knowledge management points of view. To this end, a description schema for mobile knowledge portals is introduced. In the framework of this schema, both knowledge management and mobile technology aspects and their impacts both on the user behaviour and on the internal structure and functionalities of the portals are taken into consideration. The potentials of mobile technology to leverage the knowledge portals functionalities are discussed in detail. In conclusion, potentials and future trends in development of mobile knowledge portals are discussed.
This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on “climate change” as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.
An integrated semantic dissemination system based on dynamic taxonomies is presented. The system supports conceptual information pull through an easily understood visual interface. A similar interface is used to express user interests at a conceptual level so that precise push strategies can be implemented. This system is currently used to manage the announcements coming from DBWorld, one of the best-known computer science research mailing lists, but it can be easily adapted to the dissemination needs of very diverse application areas ranging from e-government, to e-commerce, personalized news, etc.
Bibster is a semantics-based Peer-to-Peer system for exchanging bibliographic data among researchers. Bibster exploits ontologies in data storage, query formulation, query routing and answer presentation. While the original Bibster system assumed a globally shared domain ontology, we here describe extensions to the Bibster system, that allow to learn personalized ontologies from the local bibliographic metadata. These personal ontologies can not only be used for subsequently classifying the bibliographic metadata, but also for supporting an improved query refinement process.
In the digital planning process of a manufacturing plant, several partners like OEM, prime contractor and its subcontractors are involved. Since the partners have partially overlapping views (electricity, mechanical structure, plant controlling) on the plant to be designed, they have to exchange data during their collaboration. Due to syntactical, structural and semantical differences, data integration is necessary but also complicated. Our method of resolution comprises an ontology-based reference model, which all partners map to as well as an underlying technical infrastructure. This paper focuses on the methodology for constructing an ontology-based reference model in digital production engineering.
Destinations are at the heart of travel decisions, and destination image has a significant influence on tourists’ decision-making. Many travelers acquire information via the Internet, which offers abundant information and an increasing number of tourism-related services. The impact of media coverage on destination image has attracted research attention and became particularly evident during the 2003 outbreak of SARS, the Severe Acute Respiratory Syndrome. Building upon previous research, this paper analyzes the prevalence of tourism destinations among 162 international media sites. Measuring term frequency investigates the attention a destination receives – from a general and, after contextual filtering, from a tourism perspective. Calculating the semantic orientation estimates positive or negative media influences on destination image at a given point in time. By detecting associations with country names, keyword analysis reveals the countries’ public profile, and the impact of events on media coverage.
In many areas of the medical domain, the decision process i.e. reasoning, involving health care professionals is distributed, cooperative and complex. Computer based decision support systems has usually been focusing on the outcome of the decision making and treated it as a single task. In this paper a framework for a Clinical Reasoning Knowledge Warehouse (CRKW) is presented, intended to support the reasoning process, by providing the decision participants with an analysis platform that captures and enhances information and knowledge. The CRKW mixes theories and models from Artificial Intelligence, Knowledge Management Systems and Business Intelligence to make context sensitive, patient case specific analysis and knowledge management. The knowledge base consists of patient health records, reasoning process information and clinical guidelines. Patient specific information and knowledge is continually enhanced by adding results of analysis. Context sensitive analysis is done by retrieving similar patient cases and guidelines from the knowledge base in a case based fashion.
Information quality is an important aspect in decision-making processes. Since high quality information enables possessors to make effective and more efficient decisions, each decision maker should be able to access high quality information. Accordingly, the aims of this paper are twofold. First, we suggest a definition of information quality tailored to the context of decision-making. The definition of information quality is derived from the semiotic concept of information and results in eight information quality criteria. Second, this paper illustrates the technological implementation of quality criteria with a special focus on the relevance of information as one important criterion of information quality. An approach will be described to search automatically for relevant documents using any document that describes the research context to set off the search process instead of a simple search string.
This document describes our current work on developing a framework which supports organizations in the successful implementation of Knowledge Management (KM). It follows the holistic approach of a KM introduction by considering technological, organizational and human aspects, as well as the organizational culture in equal measure. The framework provides recommendations based on Case-Based Reasoning (CBR) techniques and Semantic Web technologies. It supports the four processes of Aamodt & Plaza’s CBR-cycle. The best practice cases for a successful KM implementation are structured by the use of an ontology.
This paper is focused on the problem of skill matching in an organizational context. We endow the classical weighted bipartite graph approach with a semantic based assignment of arcs weight and we describe a skill matching system implementing the approach. The system takes curricula and project specifications as inputs and extracts from them individual profiles respectively offered and requested, according to an ontology modeling skill management context. The suitability of each available individual to each task to assign is evaluated based on an algorithm whose returned scores are used as arc weights. As a result the semantics of profile descriptions is taken into account in the assignment process.
Topic Maps are sophisticated indexes for dynamic, heterogeneous, structured, and unstructured information sources. In order to move Topic Maps towards the mainstream, the automatic generation of Topic Maps and its integration in the users working environment and processes must be improved. Our described approach for Topic Map generation is based on terminology extraction with relevance feedback, which improves our previous approaches especially for small corpora. The relation between the Subjects and Topics is the core of the Topic Map theory. We propose a methodology for the proper integration of this underlying theory in the generation of Topic Maps in order to obtain real interchangeable Topic Maps. The framework TOMATO is a scientific prototype which realises the described functionality and offers interfaces for integration in applications and web-based interfaces.
This paper describes our current work in supporting the ad-hoc composition of teams, recruited from independent departments in an organisation, in order to solve a specific customer request. We propose the usage of a web-based decision support system using ontologies as a conceptual model for representing the organisation-specific competence portfolio. A case study shows the advantages of the proposed approach.
Through application of Web usage mining to operational Web systems, we have confirmed the effectiveness of active mining, which mines actionable knowledge as follows: First, we describe an adaptable recommendation system called the system L-R, which constructs user models as actionable knowledge by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the pages to the users based both on the user models and the Web site structures. We have evaluated the prototype system and have successfully obtained the positive effects of the mined actionable knowledge. Second, we describe another approach to constructing user models, which clusters Web access logs based on access patterns. The actionable knowledge helps to discover unexpected access paths corresponding to ill-formed Web site design. Lastly, we have successfully identified undiscovered research issues such as dynamic page recommendation when we have attempted to mine Web usage logs for operational systems.
The recent evolution of e-commerce and the astonishing growth of the Internet have increased the amount of information that scrupulous customers want to process before selecting items that meet their needs. Personalization has become an important strategy in Business to Consumer e-commerce, where knowledge about customers can be exploited in order to improve access to relevant products. This paper presents a machine learning-based approach to turn raw data about customers into knowledge about their interests. This knowledge is stored in personal profiles and is used to provide an intelligent search support.
In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This innovative system exploits the advances in handlings a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced services characterized by the tri-fold ”semantic phrasing of the request (query)”, ”unified handling” and ”personalized response”. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of the queries that users my issue, and the context determined by user profiles. It also provides a personalization process of the response in order to provide end-users with desired information. From a technical point of view, the FAETHON system plays the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to the new MPEG standards.
In this paper we presented a framework for the query refinement which is driven by the user’s information needs. Based on the analyses of the real IR case studies we gathered the requirements and modeled the query refinement process as the process of changing query ambiguity according to a user ‘s need. We define several semantic and structural ambiguities, implied by the used vocabulary and information repository, respectively. As our evaluation study shows, the usability of the approach overcomes the traditional information retrieval problems and enables the realisation of an e-shop-assistant in the e-commerce applications.
SENEKA (Service-Netzwerke für und Weiterbildungsprozesse) is a large-scale entrepreneurial and research programme, funded by the German Federal Ministry of Research and Education. The programme aims not only to improve the management of information flow and knowledge creation, but seeks to re-design processes of innovation and research. The pool of participating companies in this programme consists mainly of SMEs empowering their capabilities and competencies with regards to innovation management. Approximately 30 companies, mainly located in Germany and research institutes from all over Germany are working in different fields of activity seeking to find new methods and tools to improve information flow and knowledge management processes. Recent trends in information and knowledge management describe challenges the participating companies try to meet while developing these new methods and tools.
Current E-Learning solutions are not sufficiently aware of the context of the learner, i.e. the individual characteristics, the organization and the work processes and tasks. This can be achieved by modular learning objects and semantical metadata for their contextualization. This allows to deliver learning material that is relevant to the current situation of the learner. This paper presents the general approach and the architecture.
The present state of research on competence management does not provide any suitable model that can be used in practice. This article presents a model for integrated competence management, which gives approaches from both cognitive science and organizational science a practical framework of action.
Documentation analysis and organisation are vital to the researcher when trying to understand the evolution of patrimonial edifices and sites. Documentary sources provide partial evidences from which the researcher will infer possible scenarios on how an edifice may have changed throughout the centuries. Still, in the field of the architectural heritage, there is a gap to fill between proven data management technologies that provide solutions for documentation handling, and geometric modelling techniques that underlie reconstruction efforts. Documentation is organised with regards to what the documents are, books, illustrations, etc… Virtual renderings feature a geometry that bears no link to the documentation’s analysis. Our contribution introduces a solution for attaching the documentation to architectural concepts that represent physical beings used in the edifice’s structure, and this without modifications on existing documentation descriptions. 3D scenes can then be used as one of the means to access or visualise the information we hold on the edifice’s or site’s evolution.
This paper will present a new visual information retrieval system for metadata and the interaction techniques of the new system. The abundance of information we get while analyzing search results of an arbitrary query has to be channeled. This can be done by different visualizations and filter techniques. We use a Scatterplot in combination with a so called SuperTable to support the process of finding relevant information in an intuitive yet multifunctional way. Visual filters and the interaction between the visualizations play an important role. By examples from a web and a movie database search features are demonstrated.
This paper presents an approach to supporting the exchange of knowledge in communities of practice that connect experts from different fields of expertise. The developed system allows unobtrusive construction of personalised knowledge maps that capture implicit knowledge of individuals and groups of users and make it usable for collaborative structuring of shared information repositories. The personalised maps also reflect the global patterns of knowledge exchange in the community which allows the extraction of a shared conceptual structure that connects knowledge across different individuals and groups of users. To this end techniques for self-organised clustering are combined with methods for supervised learning and collaborative filtering. Application scenarios include automatic generation of personalised knowledge portals, collaborative knowledge management and the construction of shared ontologies and topic networks. The real-world testbed is the Internet platfom netzspannung.org.
There is an immense number of information resources on the Internet that can be utilized free of charge. So many knowledge workers try to make use of this information in their daily tasks. Nevertheless, it is very hard to find the relevant information in the Internet by using the full-text retrieval techniques which are offered by most existing search engines. This paper demonstrates that Thesauri, which have been used in established online retrieval systems for a long time, also open up new methods for the automated search for information in the Internet. In addition, thesaurus-like structures known as Gazetteers allow handling geographical references of information resources in a very effective way. The knowledge represented in thesauri and gazetteers can be used to process a variety of thematic and geographical queries and to retrieve the information of interest from the Internet. Comfortable ways of specifying queries can be offered to the users, e.g., by navigating in a hierarchical tree of descriptors, by using synonymous, related or foreign-language terms rather than fixed elements of a controlled vocabulary, or by indicating a geographical region of interest on a cartographic map.
In addition to the general principles, examples of powerful query processors and advanced user interfaces are presented which demonstrate the effective usage of the knowledge stored in thesauri and gazetteers. The implemented solutions turn out to be considerably more comfortable than the “black box search” offered by most existing library catalogs and Internet search engines.
This article outlines new technologies in the areas of automated expertise finding, expert network discover, virtual place-based collaboration, and automated question answering. We illustrate each of these areas with implemented and in some cases empirically evaluated systems. Collectively, these illustrate new methods for automatic discovery of knowledge, experts, and communities in an effective and efficient manner.
This paper reports on a study examining attorneys’ and law librarians’ use of their memory and information they record externally in searching for, using, and sharing legal information. The paper suggests automatically and manually recording search histories and basing user interface tools on this information to support mental model building and knowledge sharing in the legal information domain. The research described is part of the author’s dissertation research [1] that examined the use of search histories in legal information seeking and use, and proposed interface design recommendations for information systems. While searching for and using information, attorneys learn about legal topics and use this knowledge in their work. They create mental models and share their new knowledge with colleagues. Computers can automatically record human-computer interaction events. This information can help searchers represent and share new knowledge. The recorded information can be provided back to the user through the user interface to support searching for and using information, learning about the subject matter and sharing this knowledge with others. In this study, attorneys and law librarians were interviewed and observed to assess their use of their memory and external memory aids while searching for and using legal information. The results reported here focus on the role of interaction histories and history-based interface tools in supporting mental model development of legal information seekers of a topical area and sharing this information with other users.
On the basis of an example gained from the perspective of a person reading Intellectual Capital (IC) reports this paper explains the method of BibTechMonTM which is based on an analysis of the co-occurrence of different terms within databases and the algorithm to visualise the results [Kopcsa, A., Schiebel, E. (1998b)]. The application of this method for the IC report is currently a major step in improving the IC reporting system within ARC Seibersdorf research GmbH. In this paper the advantages and potentials of using BibTechMonTM in the context of IC reporting will be demonstrated by means of the 2001 IC report of ARC Seibersdorf research GmbH.
The main goal of this research is to improve Information Retrieval Systems by enabling them to generate search outcomes that are relevant and customized to each specific user. Our proposal advocates the use of Instance Based Reasoning during the information retrieval process. When conducting a search, the system retrieves a previous similar search experience and traces back previous human reasoning and behavior and then replicates it in the current situation. Thus, user information retrieval experiences or instances are saved to be reused in future similar cases. The resulting cooperative memory is used for user query expansion.
In order to improve the information retrieval experience, we propose to conceptualize and model both the user profile, and the information retrieval process. This leads us to define some similarity functions between user profiles and information retrieval situations. The reuse of past experiences serves to enrich the initial user query by words from documents found in similar
cases. Unlike the classical Rocchio method, these documents are those already judged as valid by users with similar profile and in similar search situation. The value this method brings to the user is an increasing relevance of the search outcomes while reducing user interaction with the system.
This method has been implemented in the COSYDOR (Cooperative System for Document Retrieval) prototype based on Intermedia (Oracle 8i). Tests and evaluations have been performed on the COSYDOR prototype using the test corpus of TREC (Text Retrieval Conference) and its standard procedures for performance analysis and benchmarking. The results of these analyses show a significant improvement of performance in the first search iterations compared to the Intermedia benchmark.
Starting from text corpus analysis with linguistic and statistical analysis algorithms, an infrastructure for text mining is described which uses collocation analysis as a central tool.
This text mining method may be applied to different domains as well as languages. Some examples taken form large reference databases motivate the applicability to knowledge management using declarative standards of information structuring and description. The ISO/IEC Topic Map standard is introduced as a candidate for rich metadata description of information resources and it is shown how text mining can be used for automatic topic map generation.