Information Retrieval by Semantic Similarity

TLDR

Semantic similarity measures conceptual relatedness between terms that may not share lexical form, typically by mapping them to an ontology and analyzing their relationships. The study investigates computing semantic similarity between natural language and medical terms using WordNet and MeSH, and proposes the Semantic Similarity based Retrieval Model (SSRM) for document retrieval. The authors implemented and evaluated popular semantic similarity methods on WordNet and MeSH, incorporated the best-performing method into SSRM, and applied the model to the OHSUMED TREC collection. Experiments showed that SSRM outperforms traditional lexical matching models and existing ontology-based semantic retrieval methods.

Abstract

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.