Authors: Leo Sauermann, Ludger van Elst, Andreas Dengel

Abstract: This paper presents the concept and realization of a Personal I nformation
Model (PIMO). A PIMO is used to represent a single users’ concepts, such as projects,
tasks, contacts, organizations, allowing files, e-mails, and other resources of interest
to the user to be categorized. This categorization using multiple criteria was used to
integrate information across different applications and file formats. Based on RDF/S,
multiple layers were defined: an upper-layer for a minimal set of generic concepts, a
mid-layer for refinements, and a user-layer for concepts of the individual user. Our
approach was deployed and used in several research projects. The PIMO helps users
to categorize resources for Personal Information Management (PIM), it is intended to
be the integrative part in personalized systems, such as Social Semantic Desktops.1

Key Words: personal information management, ontologies, personalization

Category: H.1.1, H.3.3

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Authors: Alex Kohn, Francois Bry, Alexander Manta

Abstract: This paper introduces YASE, a domain-aware search agent with learning
capabilities. Initially built for the research community of Roche Penzberg, YASE proved to be
superior to standard search engines in the company environment due to the introduction of
some simple principles: personalized ranking based on a user’s role and organizational
embedding, automatic classification of documents by using domain knowledge and learning
from search history. While the benefits of the learning feature need more time to be fully
realized, the other two principles have proved to be surprisingly powerful.

Keywords: search agent / engine, metadata, personalization, information retrieval, YASE

Categories: H.3.3, H.4.0, L.2.2

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Authors: Malinka Ivanova, Ekaterina Vasilyeva,Inga Beleviciute

Abstract: The knowledge is defined as combination and organization of data and information
in given context and Knowledge Management (KM) provides capturing, storing and reusing of
knowledge objects. In Web 2.0 world the knowledge is represented in form of microcontent
object and KM 2.0 proposes creation, sharing and leveraging the microknowledge in a
collaborative way. The microknowledge in Web 2.0 network can be controlled through
designing the instructional strategies that will provide user learning paths and activities and that
will give possibilities for sharing of those same learning activities and microknowledge with
others. The present paper is aimed to reflect of the research needs and the new challenges in the
mentioned above three areas: KM, Web 2.0 technologies and Learning Design (LD). It is
focused on designing of personalized learning using IMS LD elements. The paper analyzes the
possibilities of applying Web 2.0 technologies for defining a broaden set of activities and
creating the rich environments with microknowledge objects and web services in one successful
scenario.

Keywords: Knowledge management, Web 2.0, IMS Learning Design,
personalization, adaptation

Categories: H.5.1, H.5.2, H.5.3, H.5.4, H.3.5, H.1.2

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Authors: Stephan Kaiser, Gordon Müller-Seitz

Abstract: In recent years, both practitioners and scholars alike have emphasized the
importance of organizational knowledge and learning as a source of competitive advantage. In
this connexion, the integration of information technologies to foster these organizational
sources has gained considerable attention. However, especially due to the innate characteristics
of tacit knowledge, its handling is deemed to be extraordinarily difficult. This results primarily
from the fact that tacit knowledge can only be disseminated via intricate interpersonal
processes. Socialization is viewed as an appropriate approach but to date this is regarded as
incommensurate with most existing information technologies. In this paper we argue that
corporate weblogs – i.e. personalized and informal Internet publications referring to corporate
concerns – constitute a viable option to deal with this dilemma while incorporating the
possibility to codify as well as personalize tacit knowledge. By illuminating how to transcend
the traditional impasse, we intend to contribute to knowledge management research and
practitioners alike.

Keywords: Knowledge Management, Corporate Weblogs, Knowledge Diffusion,
Personalization, and Codification

Categories: H.0, H.2.m, H.3.m, H.4.m

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Authors: Ekaterina Vasilyeva, Mykola Pechenizkiy,Seppo Puuronen

Abstract: A number of recent studies have contributed to Knowledge Management (KM) and
E-learning integration. They are mainly based on organizational learning analysis. In this paper,
KM is discussed from the viewpoint of adaptation in e-learning systems. The main components
of adaptive e-learning system are discussed with respect to the KM processes. We analyze users
and developers of adaptive e-learning systems and the knowledge, with which they operate. We
present our view of knowledge and meta-knowledge concepts in the context of adaptive elearning
systems. The role of meta-knowledge as a contextual knowledge is emphasized for
adaptation in e-learning. The paper discusses the challenges of KM in adaptive e-learning
systems and analyses the main KM processes that should be introduced in adaptive e-learning
systems.

Keywords: Adaptive e-learning systems, personalization, knowledge management, contextual
knowledge, user interfaces

Category: H.5.1, H.5.2, H.5.3, H.5.4, H.3.5, H.1.2

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Authors: Giovanni Semeraro, Paquale Lops, Marco Degemmis

Abstract: 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.

Key Words: personalization, user profiling, intelligent search and retrieval, Bayesian learning,
learning from labeled and unlabeled

Category: H.3.3, H.3.5

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