Taxonomy-Based Information Content and Wordnet-Wiktionary-Wikipedia Glosses for Semantic Relatedness
Authors | Mohamed Ben Aouicha Mohamed Ali Hadj Taieb Abdelmajid Ben Hamadou |
|---|---|
Publication date | 2016 |
DOI | 10.1007/s10489-015-0755-x |
Links | Original |
Taxonomy-Based Information Content and Wordnet-Wiktionary-Wikipedia Glosses for Semantic Relatedness - scientific work related to Wikipedia quality published in 2016, written by Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb and Abdelmajid Ben Hamadou.
Overview
Computing the semantic similarity/relatedness between terms is an important research area for several disciplines, including artificial intelligence, cognitive science, linguistics, psychology, biomedicine and information retrieval. These measures exploit knowledge bases to express the semantics of concepts. Some approaches, such as the information theoretical approaches, rely on knowledge structure, while others, such as the gloss-based approaches, use knowledge content. Firstly, based on structure, authors propose a new intrinsic Information Content (IC) computing method which is based on the quantification of the subgraph formed by the ancestors of the target concept. Taxonomic measures including the IC-based ones consume the topological parameters that must be extracted from taxonomies considered as Directed Acyclic Graphs (DAGs). Accordingly, authors propose a routine of graph algorithms that are able to provide some basic parameters, such as depth, ancestors, descendents, Lowest Common Subsumer (LCS). The IC-computing method is assessed using several knowledge structures which are: the noun and verb WordNet “is a” taxonomies, Wikipedia Category Graph (WCG), and MeSH taxonomy. Authors also propose an aggregation schema that exploits the WordNet “is a” taxonomy and WCG in a complementary way through the IC-based measures to improve coverage capacity. Secondly, taking content into consideration, authors propose a gloss-based semantic similarity measure that operates based on the noun weighting mechanism using IC-computing method, as well as on the WordNet, Wiktionary and Wikipedia resources. Further evaluation is performed on various items, including nouns, verbs, multiword expressions and biomedical datasets, using well-recognized benchmarks. The results indicate an improvement in terms of similarity and relatedness assessment accuracy.
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Wikipedia Quality
Aouicha, Mohamed Ben; Taieb, Mohamed Ali Hadj; Hamadou, Abdelmajid Ben. (2016). "[[Taxonomy-Based Information Content and Wordnet-Wiktionary-Wikipedia Glosses for Semantic Relatedness]]". Springer US. DOI: 10.1007/s10489-015-0755-x.
English Wikipedia
{{cite journal |last1=Aouicha |first1=Mohamed Ben |last2=Taieb |first2=Mohamed Ali Hadj |last3=Hamadou |first3=Abdelmajid Ben |title=Taxonomy-Based Information Content and Wordnet-Wiktionary-Wikipedia Glosses for Semantic Relatedness |date=2016 |doi=10.1007/s10489-015-0755-x |url=https://wikipediaquality.com/wiki/Taxonomy-Based_Information_Content_and_Wordnet-Wiktionary-Wikipedia_Glosses_for_Semantic_Relatedness |journal=Springer US}}
HTML
Aouicha, Mohamed Ben; Taieb, Mohamed Ali Hadj; Hamadou, Abdelmajid Ben. (2016). "<a href="https://wikipediaquality.com/wiki/Taxonomy-Based_Information_Content_and_Wordnet-Wiktionary-Wikipedia_Glosses_for_Semantic_Relatedness">Taxonomy-Based Information Content and Wordnet-Wiktionary-Wikipedia Glosses for Semantic Relatedness</a>". Springer US. DOI: 10.1007/s10489-015-0755-x.
