Title :
Ranking Documents Semantically Using Ontological Relationships
Author :
Aleman-Meza, Boanerges ; Arpinar, Budak I. ; Nural, Mustafa V. ; Sheth, Amit P.
Author_Institution :
Rice Univ., Houston, TX, USA
Abstract :
Although arguable success of today´s keyword based search engines in certain information retrieval tasks, ranking search results in a meaningful way remains an open problem. In this work, the goal is to use of semantic relationships for ranking documents without relying on the existence of any specific structure in a document or links between documents. Instead, real-world entities are identified and the relevance of documents is determined using relationships that are known to exist between the entities in a populated ontology. We introduce a measure of relevance that is based on traversal and the semantics of relationships that link entities in an ontology. We expect that the semantic relationship-based ranking approach will be either an alternative or a complement to widely deployed document search for finding highly relevant documents that traditional syntactic and statistical techniques cannot find.
Keywords :
information retrieval; ontologies (artificial intelligence); search engines; information retrieval tasks; ontological relationships; search engines; search results; semantic relationship-based ranking approach; Autism; Impedance matching; Joining processes; Ontologies; Patents; Semantics; Vaccines;
Conference_Titel :
Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on
Conference_Location :
Pittsburgh, PA
Print_ISBN :
978-1-4244-7912-2
Electronic_ISBN :
978-0-7695-4154-9
DOI :
10.1109/ICSC.2010.47