Weighted Ontology for Semantic Search

Authors:
Anna Formica;Michele Missikoff;Elaheh Pourabbas;Francesco Taglino
Affiliations:
Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Rome, Italy 00185;Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Rome, Italy 00185;Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Rome, Italy 00185;Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Rome, Italy 00185
Venue:
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Year:
2008

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Abstract

This paper presents a method, SemSim , for the semantic search and retrieval of digital resources (DRs) that have been previously annotated. The annotation is performed by using a set of characterizing concepts, referred to as features , selected from a reference ontology. The proposed semantic search method requires that the features in the ontology are weighted. The weight represents the probability that a resource is annotated with the associated feature. The SemSim method operates in three stages. In the first stage, the similarity between concepts (consim ) is computed by using their weights. In the second stage, the concept weights are used to derive the semantic similarity (semsim ) between a user request and the DRs. In the last stage, the answer is returned in the form of a ranked list. An experiment aimed at assessing the proposed method and a comparison against a few among the most popular competing solutions is given.