Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Semantic similarity methods in wordNet and their application to information retrieval on the web
Proceedings of the 7th annual ACM international workshop on Web information and data management
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
A Graph Modeling of Semantic Similarity between Words
ICSC '07 Proceedings of the International Conference on Semantic Computing
Semantic Relatedness Measure Using Object Properties in an Ontology
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Non-classical lexical semantic relations
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A semantic similarity metric combining features and intrinsic information content
Data & Knowledge Engineering
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Semantic Relatedness Measurement (SRM) is one of the most important applications of reasoning by ontologies and different disciplines of AI, e.g. Information Retrieval, are firmly tied to it. The accuracy of SRM by lexical resources is largely determined by the quality of the knowledge modeling by the knowledge base. The limited types of relations modeled by ontologies have caused most of the SRM methods to be able to detect and measure only a few special types of semantic relationships that is very far from the concept of semantic relatedness in human brain. Concepts of lexical resources are usually accompanied with a plain text narratively defines the concept. The information included in the definition of concepts sound very promising for SRM. This paper intends to treat this information as formal relations to improve SRM by distance-base methods. In order to do so, concepts glosses are mined for the semantic relations that are not modeled by the ontology. Then, these relations are employed in combination with classic relations of the ontology for semantic relatedness measurement according to the shortest path between concepts. Our evaluation demonstrated qualitative and quantitative improvement in detection of previously unknown semantic relationships and also stronger correlation with human judgment in SRM.