@inproceedings{Hawalah2011,
abstract = {The need of determining the degree of semantic similarity, relatedness or distance between two concepts within the same ontology or two different ontologies is becoming an increasingly important task in the field of Information Retrieval. Although a great attention has been paid to design semantic similarity/relatedness methods based on taxonomies, there has been little discussion about the design of semantic similarity/relatedness methods based on ontologies. In this paper we introduce a novel graph-based semantic relatedness approach to calculate semantic relatedness considering both hierarchical and non-hierarchical concepts in an ontology. In addition, our approach considers some important properties such as different relation types, concepts' depth and distance that play an essential role in measuring semantic relatedness. Three experimental studies are provided to first illustrate that our approach give a different results than other measures in the literature. Then, we compare our approach against existed methods using a benchmark dataset, and finally, evaluate our approach by using a real ontology and compare the predictions of our semantic relatedness approach against the human-subject judgment. The results in all the experiments show a considerable improvement against traditional taxonomy-based measures.},
author = {Hawalah, Ahmad and Fasli, Maria},
booktitle = {Proceedings of the International Conference on Web Intelligence, Mining and Semantics},
keywords = {SML-LIB-BIBLIO,lang:ENG,ontology,semantic relatedness,semantic similarity,web mining},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {29},
publisher = {ACM},
title = {{A Graph-based Approach to Measuring Semantic Relatedness in Ontologies}},
url = {http://portal.acm.org/citation.cfm?id=1988722},
year = {2011}
}