Trust Networks on the Semantic WebJennifer Golbeck1, Bijan Parsia1, James Hendler11University of Maryland, College ParkA. V. Williams BuildingCollege Park, Maryland [email protected],[email protected],[email protected]. The so-called "Web of Trust" is one of the ultimate goals of theSemantic Web. Research on the topic of trust in this domain has focused largelyon digital signatures, certificates, and authentication. At the same time, there isa wealth of research into trust and social networks in the physical world. In thispaper, we describe an approach for integrating the two to build a web of trust ina more social respect. This paper describes the applicability of social networkanalysis to the semantic web, particularly discussing the multi-dimensionalnetworks that evolve from ontological trust specifications. As a demonstrationof algorithms used to infer trust relationships, we present several tools thatallow users to take advantage of trust metrics that use the network.1 Introduction"Trust" is a word that has come to have several very specific definitions on theSemantic Web. Much research has focused on authentication of resources, includingwork on digital signatures and public keys. Confidence in the source or author of adocument is important, but trust, in this sense, ignores many important points.Just because a person can confirm the source of documents does not have any explicitimplication about trusting the content of those documents. This project addresses“trust” as credibility or reliability in a much more human sense. It opens up the doorfor questions like “how much credence should I give to what this person says about agiven topic,” and “based on what my friends say, how much should I trust this newperson?"In this paper, we will discuss the application of a social network to the semantic web.Section 2 discusses how to build a meaningful social network from the architecture ofthe semantic web, and how it conveys meaning about the structure of the world.Section 3 will describe the implementation of such a network. We describe a sampleontology, an algorithm for computing trust in a network, and present tools that usethis network to provide users with information about the reputation of others.1.2 Related WorkThis paper uses techniques developed in the field of social network analysis, andapplies that to the issue of trust on the semantic web. This section describes the mostrelevant works from each area.1.2.1 Social NetworksSocial networks have a long history of study in a wide range of disciplines. The moremathematical of the studies have appeared in the "Small World" literature. The workon Small Worlds, also commonly known as "Six Degrees of Separation" originatedout of Stanley Milgram's work in the 1960s. His original studies indicated that anytwo people in the world were separated by only a small number acquaintances(theorized to be six in the original work) [18]. Since then, studies have shown thatmany complex networks share the common features of the small-world phenomenon:small average distance between nodes, and a high connectance, or clusteringcoefficient [21].Small world networks have been studied in relation to random graphs [29]. For socialsystems, both models have been used to describe phenomena such as scientificcollaboration networks [20], and models of game theory [29]. The propagation ofeffects through these types of networks has been studied, particularly with respect tothe spread of disease [19, 7]. The web itself has shown the patterns of a small worldnetwork, in clustering and diameter [4, 1].Viewing the current web as a graph, where each page represents a node, and thehyperlinks translate to directed edges between nodes, has produced some interestingresults. The main focus of this research has been to improve the quality of search[7,6,5,8,14,26]. Other work has used this structure for classification [9] andcommunity discovery [15].1.2.2 Trust on the Semantic WebYolanda Gil and Varun Ratnakar addressed the issue of trusting content andinformation sources [12]. They describe an approach to derive assessments aboutinformation sources based on individual feedback about the sources. As users addannotations, they can include measures of Credibility and Reliability about astatement, which are later averaged and presented to the viewer. Using the TRELLISsystem, users can view information, annotations (including averages of credibility,reliability, and other ratings), and then make an analysis.Calculating trust automatically for an individual in a network on the web is partiallyaddressed in Raph Levin's Advogato project [17]. His trust metric uses groupassertions for determining membership within a group. The Advogato [3] website, forexample, certifies users at three levels. Access to post and edit website information iscontrolled by these certifications. On any network, the Advogato trust metric isextremely attack resistant. By identifying individual nodes as "bad" and finding anynodes that certify the "bad" nodes, the metric cuts out an unreliable portion of thenetwork. Calculations are based primarily on the good nodes, so the network as awhole remains secure.2 Networks on the Semantic WebStudying the structure of the hypertext web can be used to find community structurein a limited way. A set of pages clustered by hyperlinks may indicate a common topicamong the pages, but it does not show more than a generic relationship among thepages. Furthermore, pages with fewer outgoing links are less likely to show up in acluster at all because their connectance is obviously lower. These two facts make itdifficult for a person to actually see any relationship among specific concepts on theweb as it currently stands – classification is not specific enough, and it relies on heavyhyperlinking that may not be present.The Semantic Web changes this. Since the semantic data is machine-understandable,there is no need to use heuristics to relate pages. Concepts in semantically marked uppages are automatically linked, relating both pages and concepts across a distributedweb.By it's nature, the semantic web is one large graph. Resources (and literals)