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Computing information value from RDF graph properties

Published:08 November 2010Publication History

ABSTRACT

Information value has been implicitly utilized and mostly non-subjectively computed in information retrieval (IR) systems. We explicitly define and compute the value of an information piece as a function of two parameters, the first is the potential semantic impact the target information can subjectively have on its recipient's world-knowledge, and the second parameter is trust in the information source. We model these two parameters as properties of RDF graphs. Two graphs are constructed, a target graph representing the semantics of the target body of information and a context graph representing the context of the consumer of that information. We compute information value subjectively as a function of both potential change to the context graph (impact) and the overlap between the two graphs (trust). Graph change is computed as a graph edit distance measuring the dissimilarity between the context graph before and after the learning of the target graph. A particular application of this subjective information valuation is in the construction of a personalized ranking component in Web search engines. Based on our method, we construct a Web re-ranking system that personalizes the information experience for the information-consumer.

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            • Published in

              cover image ACM Other conferences
              iiWAS '10: Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
              November 2010
              895 pages
              ISBN:9781450304214
              DOI:10.1145/1967486

              Copyright © 2010 ACM

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              Publication History

              • Published: 8 November 2010

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