Matches in ScholarlyData for { ?s ?p The use of natural language identifiers as reference for ontology elements—in addition to the URIs required by the Semantic Web standards—is of utmost importance based on their predominance in the human everyday life, i.e. speech or print media. Depending on the context, different names can be chosen for one and the same element, and the same element can be referenced by different names. Here homonymy and synonymy are the main cause of the appearance of ambiguity in perceiving which concrete unique ontology element ought to be referenced by a specific natural language identifier describing an entity. We propose a novel method to resolve entity references under the aspect of ambiguity which explores only formal background knowledge represented in RDF graph structures. The key idea of our domain independent approach is to build an entity network with the most likely referenced ontology elements by constructing spanning graphs based on spreading activation (SA). Additional to the exploitation of complex graph structures we devise a new ranking technique that characterises the likelihood of entities in this network, i.e. interpretation contexts. Experiments in a highly polysemic domain show the ability of the algorithm to retrieve the correct ontology elements in almost all cases.. }
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- 43 abstract "The use of natural language identifiers as reference for ontology elements—in addition to the URIs required by the Semantic Web standards—is of utmost importance based on their predominance in the human everyday life, i.e. speech or print media. Depending on the context, different names can be chosen for one and the same element, and the same element can be referenced by different names. Here homonymy and synonymy are the main cause of the appearance of ambiguity in perceiving which concrete unique ontology element ought to be referenced by a specific natural language identifier describing an entity. We propose a novel method to resolve entity references under the aspect of ambiguity which explores only formal background knowledge represented in RDF graph structures. The key idea of our domain independent approach is to build an entity network with the most likely referenced ontology elements by constructing spanning graphs based on spreading activation (SA). Additional to the exploitation of complex graph structures we devise a new ranking technique that characterises the likelihood of entities in this network, i.e. interpretation contexts. Experiments in a highly polysemic domain show the ability of the algorithm to retrieve the correct ontology elements in almost all cases.".