Chapter 14 Approximate knowledge graph retrieval: Measures and realization

Abstract

The World Wide Web is moving to its next generation called Semantic Web. Managing and searching for web information goes beyond the conventional relational database model, as the data are semi-structured and inexact answers are often the case. This research work studies similarity and subsumption measures for matching knowledge graphs based on the non-directed and directed semantic distances between their entity types and attribute values. A knowledge base represented in RDF is constructed on an ontology that defines its concept types and relation types with their signatures. A query engine is developed and implemented to query the knowledge base, returning approximate answers with similarity and subsumption degrees. On the one hand, for a user-friendly interface and easily readable query expressions, conceptual graphs are employed at the front-end. On the other hand, in order to take the advantage of the existing platform of Sesame, the query modification tactic is first used to retrieve the knowledge graphs that are close to a query graph, before the matching degrees of those answer graphs are calculated. © 2006 Elsevier B.V. All rights reserved.