Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contributions to Semantic Web trust management through this paper are twofold. First, we introduce our classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we will devise our advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion.

Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours.

Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity between users but very often the sparseness of user profiles prevents the computation. Moreover CF doesn't take into account the reliability of the other users. In this paper we present a real world application, namely moleskiing.it, in which both of these conditions are critic to deliver personalized recommendations. A blog oriented architecture collects user experiences on ski mountaineering and their opinions on other users. Exploitation of Trust Metrics allows to present only relevant and reliable information according to the user's personal point of view of other authors trustworthiness. Di#erently from the notion of authority, we claim that trustworthiness is a user centered notion that requires the computation of personalized metrics. We also present an open information exchange architecture that makes use of Semantic Web formats to guarantee interoperability between ski mountaineering communities.

Web spamming describes behavior that attempts to deceive search engine's ranking algorithms. TrustRank is a recent algorithm that can combat web spam by propagating trust among web pages. However, TrustRank propagates trust among web pages based on the number of outgoing links, which is also how PageRank propagates authority scores among Web pages. This type of propagation may be suited for propagating authority, but it is not optimal for calculating trust scores for demoting spam sites. In this paper,

Abstract. Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, opening new opportunities by either replacing or supplementing current techniques. However, in order to provide meaningful results for recommender system applications, we expect notions of trust to clearly reflect user similarity. In this work, we therefore provide empirical results obtained from one real, operational community and verify latter hypothesis for the domain of book recommendations. 1

The number of ontologies and knowledge bases covering different domains and available on the World-Wide Web is steadily growing. As more ontologies are available, it is becoming harder, and not easier, for users to find ontologies they need. How do they evaluate if a particular ontology is appropriate for their task? How do they choose among many ontologies for the same domain? We argue that allowing users on the Web to annotate and review ontologies is an important step in facilitating ontology evaluation and reuse for others. However, opening the system to everyone on the Web poses a problem of trust: Users must be able to identify reviews and annotations that are useful for them. We discuss the kinds of metadata that we can collect from users and authors of ontologies in the form of annotations and reviews, explore the use of an Open Rating System for evaluating ontologies and knowledge sources, and present a brief overview of a Web-based browser for Protégé ontologies that enables users to annotate information in ontologies. Ontologies On The Web Scale The number of ontologies and knowledge bases covering different domains and available on the World-Wide Web is steadily growing. Ontologies constitute the backbone of the Semantic Web and their number is steadily growing. The Swoogle crawler, 1 for example, indexes more than 4000 ontologies at the time of this writing. It is commonly agreed that one of the reasons ontologies became popular is because they hold a promise of facilitating interoperation between software resources by virtue of being shared agreed-upon descriptions of domains used by different agents. Such interoperation is, for example, a key requirement for the Semantic Web to succeed. Suppose we are developing a Semantic Web service that uses an ontology. If we choose to reuse an existing ontology to support our service rather than to create a new one, we get the interoperation with the others using the same ontology “for free. ” In addition,

Abstract. Revyu.com is a live, publicly accessible reviewing and rating Web site, designed to be usable by humans whilst transparently generating machinereadable RDF metadata for the Semantic Web, based on their input. The site uses Semantic Web specifications such as RDF and SPARQL, and the latest Linked Data best practices to create a major node in a potentially Web-wide ecosystem of reviews and related data. Throughout the implementation of Revyu design decisions have been made that aim to minimize the burden on users, by maximizing the reuse of external data sources, and allowing less structured human input (in the form of Web2.0-style tagging) from which stronger semantics can later be derived. Links to external sources such as DBpedia are exploited to create human-oriented mashups at the HTML level, whilst links are also made in RDF to ensure Revyu plays a first class role in the blossoming Web of Data. The site is available at

Abstract. The problem of identifying trustworthy information on the World Wide Web is becoming increasingly acute as new tools such as wikis and blogs simplify and democratize publications. Wikipedia is the most extraordinary example of this phenomenon and, although a few mechanisms have been put in place to improve contributions quality, trust in Wikipedia content quality has been seriously questioned. We thought that a deeper understanding of what in general defines high-standard and expertise in domains related to Wikipedia – i.e. content quality in a collaborative environment – mapped onto Wikipedia elements would lead to a complete set of mechanisms to sustain trust in Wikipedia context. Our evaluation conducted on about 8,000 articles, representing 65 % of the overall Wikipedia editing activity, shows that the new trust evidence that we extracted from Wikipedia allows us to transparently and automatically compute trust values to isolate articles of great or low quality. 1

To achieve better interoperability among intelligent applications, and to relieve knowledge engineers from the burden of developing ontologies from scratch, it is critical to reuse ontologies. However, there are two main reasons why the reuse of ontologies is rare: (1) current ontology repositories allow only simple keyword-based search facilities, and (2) even when a user finds an ontology, the information about the ontology quality and (re)usability is not available.