An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
On automating Web services discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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As increasing linked datasets are progressively published on the Semantic Web, discovering the most similar entities in large linked datasets becomes crucial in many semantic applications. Conventional approaches usually draw upon either ontology taxonomy or relationships unilaterally. In this paper, we present a novel approach which utilizes node and link types together with the topology of semantic graph to derive a similarity graph from linked datasets. Firstly, semantic similarity transition is proposed to calculate the similarity between two resources. Furthermore, a system is developed to derive and visualize the similarity graph based on the calculated similarity. We apply this approach to a real-world linked dataset generated in healthcare domain and the evaluation result shows that our method yields a satisfying result in a use case of clinical decision-making.