Provenance-Based Strategies to Develop Trust in Semantic Web Applications

Linked data and Semantic Web technologies enable people to navigate across heterogeneous sources of data thus making it easier for them to explore and develop multiple perspectives for use in making decisions and solving problems. While the Semantic Web offers benefits for developers and users, several new challenges are emerging that may negatively impact users’ trust in Web-based collaborative systems. This paper describes several use cases to illustrate potential trust issues faced by Semantic Web applications, and provides a concrete example for each using a specific system we built to investigate United States Supreme Court decision making. Provenance-based solutions are proposed to develop trust and/or minimize the distrust that is provoked by the situation. While these use cases address distinct situations, they are all described in terms of how a contradiction can arise between the user’s mental model and the statements presented in the display. This commonality may be used to develop additional classes of trust-threatening use cases, and the proposed provenance-based solutions can be applied to many other Semantic Web Applications.

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The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust.

The NSF funded Theory and Practice of Accountable Systems (TPAS) Project investigates computational and social properties of information networks necessary to provide reliable assessments of compliance with rules and policies governing the use of information. In past research, we have demonstrated that achieving basic social policy goals in open information networks will require increased reliance on information accountability through after-the-fact detection of rule violations.

The goals of this effort is to design and implement a configurable and extensible semantic eScience framework. Configuration will require some research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility will be achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in a community setting, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.

The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on Data.gov and other websites. Large portion of government data published on the Web are not necessarily ready for mashups. The Tetherless World Constellation (TWC) is now publishing over 8 billions RDF triples converted from hundreds of government-related datasets from Data.gov and other sources (e.g.

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