A Semantic Matching Strategy for Very Large Knowledge Bases Integration

A Semantic Matching Strategy for Very Large Knowledge Bases Integration

Antonio M. Rinaldi (Università degli Studi di Napoli Federico II, Naples, Italy), Cristiano Russo (University of Paris-Est Creteil (UPEC), Créteil, France) and Kurosh Madani (Université Paris-Est - LISSI EA 3956, Créteil, France)
DOI: 10.4018/IJITWE.2020040101

Abstract

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.

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