Explanation Interfaces for the Semantic Web: Issues and Models

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As the Semantic Web has enabled new application capabilities, new interaction modes arise and grow in importance. Applications can now not only retrieve results but also use term meanings to derive new results. Thus, explaining results has become an important new interaction mode for Semantic Web applications. The explanation interaction mode needs to provide transparency and accountability to application results. We have developed an explanation infrastructure that can provide Semantic Web consumers (humans and agents) with explanations for results, such as where results came from and how they were derived. We have addressed explanation requirements for applications that range from intelligent analyst assistants that leverage text analytics to transparent and accountable reasoning systems that protect user privacy. In this paper, we will describe some Semantic Web user interaction requirements and paradigms that are important for Semantic Web applications.

Related Projects:

Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: 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. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.

Related Research Areas:

Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts: Semantic Web
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web