A model of knowledge based information retrieval with hierarchical concept
Journal of Documentation
Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Fast and effective query refinement
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
Concept Similarity Matching Based on Semantic Distance
SKG '08 Proceedings of the 2008 Fourth International Conference on Semantics, Knowledge and Grid
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
An empirical study of vocabulary relatedness and its application to recommender systems
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Evaluating PageRank methods for structural sense ranking in labeled tree data
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Exploring dictionary-based semantic relatedness in labeled tree data
Information Sciences: an International Journal
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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.