Trust Management for the Semantic Web

Abstract

Abstract. Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Uncertainty in Knowledge Provenance

“…Twigg [19] applied Jøsang's subjective logic based trust model to support routing decision for P2P and ad hoc networks. Golbeck et al [8] and Richardson et al [16] developed the models of trust propagation in social networks.…”

Decentralised Authoring, Annotations and Notifications for a Read-Write Web with dokieli

“…It should be possible for people to publish, share, and annotate information while ensuring their provenance, authenticity and integrity [5][6][7].…”

Rule-Based Trust Assessment on the Semantic Web

“…Our framework is focused on expressing trustworthiness of data on the open Web, evaluating trustworthiness of inferred data and on allowing mechanisms for decisions based off trust and trust computations to be declaratively specified. [22,11,12,16] discuss how trust values for users and data sources can be computed. Richardson et al enable users to maintain trust for other users and provide functions to merge these values into trust values for all users by leveraging the path of trust between users [22].…”

“…[22,11,12,16] discuss how trust values for users and data sources can be computed. Richardson et al enable users to maintain trust for other users and provide functions to merge these values into trust values for all users by leveraging the path of trust between users [22]. Kuter et al allow users to maintain trust values or trust estimates for data sources and provide a probabilistic technique to use that information to compute a trust estimate for a data source [16].…”