by Hanif Seddiqui
Abstract:
Measuring information content (IC) from the intrinsic information of an ontology is an important however a formidable task. IC is useful for further measurement of the semantic similarity. Although the state-of-art metrics measure IC, they deal with external knowl- edge base or intrinsic hyponymy relations only. A cur- rent complex form of ontology conceptualizes a class (also often called as a concept) explicitly with the help of the hyponymy classes and the asserted rela- tions and restrictions. Therefore, we propose a modi- fied metric for measuring IC intrinsically taking both the concept-to-concept and the concept-to-property relations. We evaluate our system theoretically and with experimental data. Our evaluation shows the effectiveness of our modified metric for extracting in- trinsic information content to measure semantic sim- ilarity among concepts in an ontology.
Reference:
Metric of Intrinsic Information Content for Measuring Semantic Similarity in an Ontology (Hanif Seddiqui), In Reproduction, Australian Computer Society, Inc., 2010.
Bibtex Entry:
@article{Seddiqui2010,
abstract = {Measuring information content (IC) from the intrinsic information of an ontology is an important however a formidable task. IC is useful for further measurement of the semantic similarity. Although the state-of-art metrics measure IC, they deal with external knowl- edge base or intrinsic hyponymy relations only. A cur- rent complex form of ontology conceptualizes a class (also often called as a concept) explicitly with the help of the hyponymy classes and the asserted rela- tions and restrictions. Therefore, we propose a modi- fied metric for measuring IC intrinsically taking both the concept-to-concept and the concept-to-property relations. We evaluate our system theoretically and with experimental data. Our evaluation shows the effectiveness of our modified metric for extracting in- trinsic information content to measure semantic sim- ilarity among concepts in an ontology.},
author = {Seddiqui, Hanif},
doi = {10.1.1.173.1017},
journal = {Reproduction},
keywords = {SML-LIB-BIBLIO,concept,information content,lang:ENG,ontology},
mendeley-tags = {SML-LIB-BIBLIO,information content,lang:ENG},
number = {Apccm},
pages = {89--96},
publisher = {Australian Computer Society, Inc.},
title = {{Metric of Intrinsic Information Content for Measuring Semantic Similarity in an Ontology}},
url = {http://portal.acm.org/citation.cfm?id=1862330.1862343},
year = {2010}
}