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The purpose of the paper is to review the characteristics of systems that combine P2P technology with explicit ontologies and assess the benefits of these technologies for inter‐organizational knowledge management.

We characterize existing technologies with respect to a number of aspects that are relevant to knowledge management on a technical level. We further provide an example of an existing system and categorize it according to the aspects.

We conclude that ontology‐based P2P systems are in general beneficial for distributed knowledge management systems and that the design of such systems can be guided using the aspects we distinguish.

The paper presents the first attempt to rigorously identify and discuss the design space of ontology‐based P2P systems.

The discovery of the “right” ontology or ontology part is a central ingredient for effective ontology re‐use. The purpose of this paper is to present an approach for supporting a form of adaptive re‐use of sub‐ontologies, where the ontologies are deeply integrated beyond pure referencing.

Starting from an ontology draft which reflects the intended modeling perspective, the ontology engineer can be supported by suggesting similar already existing sub‐ontologies and ways for integrating them with the existing draft ontology. This paper's approach combines syntactic, linguistic, structural and logical methods into an innovative modeling‐perspective aware solution for detecting matchings between concepts from different ontologies. This paper focuses on the discovery and matching phase of this re‐use process.

Owing to the combination of techniques presented in this general approach, the work described performs in the general case as well as approaches tailored for a specific usage scenario.

The methods used rely on lexical information obtained from the labels of the concepts and properties in the ontologies, which makes this approach appropriate in cases where this information is available. Also, this approach can handle some missing label information.

Ontology engineering tasks can take advantage from the proposed adaptive re‐use approach in order to re‐use existing ontologies or parts of them without introducing inconsistencies in the resulting ontology.

The adaptive re‐use of ontologies by finding and partially re‐using parts of existing ontological resources for building new ontologies is a new idea in the field, and the inclusion of the modeling perspective in the computation of the matches adds a new perspective that could also be exploited by other matching approaches.

One of today’s major problems in the field of e‐learning is that the creation of high‐quality content is still rather time consuming and expensive. In the past, many efforts have been made to produce educational content on the fly, but the results were mainly static blocks of recorded lecture lacking sophisticated navigation facilities. Facing this challenge the authors developed the concept of hyper‐presentations. During the live presentation content‐ and time‐based metadata is captured and stored in a lightweight and player‐independent format. With this metadata powerful navigation facilities like real time navigation and full text search in audio or video data can be generated automatically. This improves flexibility and interoperability of technical solutions, which are both key factors in the emerging rapid e‐learning market.

In this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.

Methodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.

The purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.

The authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval?

The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed.

The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document – if indeed there is any feeling at all.

This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.