REFEREE: trust management for Web applications
Selected papers from the sixth international conference on World Wide Web
Delegation logic: A logic-based approach to distributed authorization
ACM Transactions on Information and System Security (TISSEC)
Trusting Information Sources One Citizen at a Time
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Belief, information acquisition, and trust in multi-agent systems: a modal logic formulation
Artificial Intelligence
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
N3logic: A logical framework for the world wide web
Theory and Practice of Logic Programming
An Approach to Evaluate Data Trustworthiness Based on Data Provenance
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Using Dependency Tracking to Provide Explanations for Policy Management
POLICY '08 Proceedings of the 2008 IEEE Workshop on Policies for Distributed Systems and Networks
Quality-driven information filtering using the WIQA policy framework
Web Semantics: Science, Services and Agents on the World Wide Web
SAOR: Authoritative Reasoning for the Web
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A framework for trust establishment and assessment on the web of data
Proceedings of the 19th international conference on World wide web
Decentralized trust management
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
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The Semantic Web is a decentralized forum on which anyone can publish structured data or extend and reuse existing data. This inherent openness of the Semantic Web raises questions about the trustworthiness of the data. Data is usually deemed trustworthy based on several factors including its source, users' prior knowledge, the reputation of the source, and the previous experience of users. However, as rules are important on the Semantic Web for checking data integrity, representing implicit knowledge, or even defining policies, additional factors need to be considered for data that is inferred. Given an existing trust measure, we identify two trust axes namely data and rules and two trust categories namely content-based and metadata-based that are useful for trust assignments associated with Semantic Web data. We propose a meta-modeling framework that uses trust ontologies to assign trust values to data, sources, rules, etc. on the Web, provenance ontologies to capture data generation, and declarative rules to combine these values to form different trust assessment models. These trust assessment models can be used to transfer trust from known to unknown data. We discuss how AIR, a Web rule language, can be used to implement our framework and declaratively describe assessment models using different kinds of trust and provenance ontologies.