Ontology-based information content computation
Knowledge-Based Systems
Ontology-based semantic similarity: A new feature-based approach
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
Information Sciences: an International Journal
Recommendations using linked data
Proceedings of the 5th Ph.D. workshop on Information and knowledge
A semantic similarity method based on information content exploiting multiple ontologies
Expert Systems with Applications: An International Journal
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
International Journal on Semantic Web & Information Systems
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Information Content(IC) is an important dimension of assessing the semantic similarity between two terms or word senses in word knowledge. The conventional method of obtaining IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with actual usage in text as derived from a large corpus. In this paper, a new model of IC is presented, which relies on hierarchical structure alone. The model considers not only the hyponyms of each word sense but also its depth in the structure. The IC value is easier to calculate based on our model, and when used as the basis of a similarity approach it yields judgments that correlate more closely with human assessments than others, which using IC value obtained only considering the hyponyms and IC value got by employing corpus analysis.