An Efficient Approach for Semantic Relatedness Evaluation based on Semantic Neighborhood

In the context of natural language processing and information retrieval, ontologies can improve the results of the word sense disambiguation (WSD) techniques. By making explicit the semantics of the term, ontology-based semantic measures play a crucial role to determine how different ontology classes have a similar meaning. In this context, it is common to use semantic similarity as a basis for WSD. However, the measures generally consider only taxonomic relationships, which affects negatively the discrimination of two ontology classes that are related by the other relationship types. On the other hand, semantic relatedness measures consider diverse types of relationships to determine how much two classes on the ontology are related. However, these measures, especially the path-based approaches, has as the main drawback a high computational complexity to be calculated in query execution time. Also, for both types of semantic measures, it is unpractical to store all similarity or relatedness values between all ontology classes in memory, especially for large ontologies. In this work, we propose a novel approach based on semantic neighbors that aim to improve the query time in path-based semantic measures without losing their effectiveness in relatedness analysis. We also propose an efficient algorithm to calculate the semantic distance between two ontology classes. We evaluate our proposal in WSD using a pre-existent domain ontology for well-core description. This ontology contains 929 classes related to rock facies and a set of sentences from four different corpora about geology in the Oil&Gas domain. In the experiments, we compared our approach with state-of-the-art semantic relatedness measures, such as path-based, feature-based, information content, and hybrid methods regarding the F-score, query time and the total number of classes in memory. The experimental results show that the proposed method obtains F-score gains in WSD, as well as an improvement in the query time concerning the traditional path-based approaches. Also, we reduce the total number of classes stored in memory for each ontology class.