The purpose of knowledge mapping is a better orientation in a given domain and accessing knowledge of the right people at the right time. Collaboration between students and academics is desired and knowing each other’s work or research objectives can signi cantly improve it. Our focus is to improve collaboration at University of Hradec Kralove by collaborative knowledge mapping. University’s knowl-edge mapping can be based on individual’s research objectives as well as sharing related documents. We propose a conceptual model for a platform that would allow students and academics to work with a complex set of relationships structured according to the Topic Map standard.
Our goal is increasing the users’ value and experience and decreasing the implementation time for web portals. To achieve this goal we adopt a subject-centric perspective on information architecture. The fundament of this approach is that portals should be driven by subject-centric models of the portals’ domains. Out of these domain models, the interaction and interface design of the portals is self-evident. Amongst others, the international industry standard Topic Maps is a portal technology and an implementation of the subjectcentric modelling paradigm. With ActiveTM we introduce a technology, which implements a Model-driven approach to automatically create domain-customised, subject-centric portal engines, based on Topic Maps. ActiveTM has proved as technique for reducing the implementation cost of portals enormously and the implied subject-centricness increases the users’ value and experience significantly.
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.
On the one hand models can be used as navigational tools respecting mental processing capabilities of persons. On the other hand models can be analyzed automatically by information systems to deduce relevant content for knowledge management IT-components as E-Learning-Applications, KM-Portals, document management systems, etc. Therefore models of knowledge intensive business processes are a natural integration layer for persons and information systems providing the relevant context to interpret and handle information the right way. It has only to be solved how to interface these models efficiently from a person as well as from an information system point of view.
The existing semantic gaps in ontologies are the reason why the challenges in interoperability and integrations tasks within the Semantic Web are often missed. It is due to the fact, that each ontology inherently implies a set of different model types. We argue that an ontology has to disclose the modelling method which intentionally defines the model type used in an application. This paper proposes a solution based on a generic, workflow-based description of the modelling method: the Modelling Workflow Patterns (MWP). Based on Petri nets as information model, MWPs can be processed by generic interpreters to create valid instances of the specified model type. This paper presents an implemented architecture consuming workflow modelling patterns for Topic Maps and OWL ontologies.
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.