Abstract—The Web of Data cannot be a trustworthy data source unless an approach for evaluating the quality of data on the Web is established and integrated as part of the data publication and access process. In this paper, we propose an approach of using provenance information about the data on the Web to assess their quality and trustworthiness. Our contributions include a model for Web data provenance and an assessment method that can be adapted for specific quality criteria. We demonstrate how this method can be used to evaluate the timeliness of data on the Web, to reflect how up-to-date the data is. We also propose a possible solution to deal with missing provenance information by associating certainty values with calculated quality values. I.

A Community of Interest is a specific type of Community of Practice. It is formed by a group of individuals who share a common interest or passion. These people exchange ideas and thoughts about the given passion. However, they are often not aware of their membership to the community, and they may know or care little about each other outside of this clique. This paper describes a proposal to automatically identify Communities of Interest from the tastes and preferences expressed by users in personal ontology-based profiles. The proposed strategy clusters those semantic profile components shared by the users, and according to the clusters found, several layers of interest networks are built. The social relations of these networks might then be used for different purposes. Specifically, we outline here how they can be used to model group profiles and make semantic content-based collaborative recommendations.

Abstract. Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks. 1

A Community of Interest is a specific type of Community of Practice. It is formed by a group of individuals who share a common interest or passion. These people exchange ideas and thoughts about the given passion. However, they are often not aware of their membership to the community, and they may know or care little about each other outside of this clique. This paper describes a proposal to automatically identify Communities of Interest from the tastes and preferences expressed by users in personal ontology-based profiles. The proposed strategy clusters those semantic profile components shared by the users, and according to the clusters found, several layers of interest networks are built. The social relations of these networks might then be used for different purposes. Specifically, we outline here how they can be used to model group profiles and make semantic content-based collaborative recommendations.

Abstract Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.

and other research outputs Computing word-of-mouth trust relationships in social