TITLE:
An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies
AUTHORS:
Gbede Sylvain Gbame, Maho Wielfrid Morie, Konan Marcelin Brou
KEYWORDS:
Ontology, Data Structure, Similarity Measure, Concepts, Information Content
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.11,
November
10,
2023
ABSTRACT: Ontologies have been used for several years in life
sciences to formally represent concepts and reason about knowledge bases in
domains such as the semantic web, information retrieval and artificial
intelligence. The exploration of these domains for the correspondence of
semantic content requires calculation of the measure of semantic similarity
between concepts. Semantic similarity is a measure on a set of documents, based
on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or
more ontologies. The similarity between concepts is also a quantitative measure
of information, calculated based on the properties of concepts and their
relationships. This study proposes a method for finding similarity between
concepts in two different ontologies based on feature, information content and
structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity
between two concepts from different ontologies based on information content and
the set of common superconcepts, which represents the set of
common parent concepts. We simulated our method on datasets.
The results show that our measure provides similarity values that are better
than those reported in the literature.