On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
Contextual correlates of synonymy
Communications of the ACM
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
COOPIS '96 Proceedings of the First IFCIS International Conference on Cooperative Information Systems
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A Binary Linear Programming Formulation of the Graph Edit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
A semantic similarity metric combining features and intrinsic information content
Data & Knowledge Engineering
Measuring semantic similarity between biomedical concepts within multiple ontologies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A survey of graph edit distance
Pattern Analysis & Applications
Semantic Clustering Using Multiple Ontologies
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
An ontology-based measure to compute semantic similarity in biomedicine
Journal of Biomedical Informatics
Content annotation for the semantic web: an automatic web-based approach
Knowledge and Information Systems
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Biomedical Informatics
Ontology-based semantic similarity: A new feature-based approach
Expert Systems with Applications: An International Journal
Relevance criteria for data mining using error-tolerant graph matching
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Journal of Biomedical Informatics
Ontology-based semantic clustering
AI Communications
Information Sciences: an International Journal
Semantic similarity estimation from multiple ontologies
Applied Intelligence
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
International Journal on Semantic Web & Information Systems
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A key application of ontologies is the estimation of the semantic similarity between terms. By means of this assessment, the comprehension and management of textual resources can be improved. However, most ontology-based similarity measures only support a single input ontology. If any of the compared terms do not belong to that ontology, their similarity cannot be assessed. To solve this problem, multiple ontologies can be considered. Even though there are methods that enable the multi-ontology similarity assessment by means of integrating concepts from different ontologies, most of them are based on simple terminological and/or partial matchings. This hampers similarity measures that exploit a broad set of taxonomic evidences of similarity, like feature-based ones. In this paper, we tackle this problem by proposing a method to identify all the suitable matchings between concepts of different ontologies that intervene in the similarity assessment. In addition to the obvious terminological matching, we exploit the ontological structure and the notion of concept subsumption to discover non-trivial equivalences between heterogeneous ontologies. Our final goal is to enable the accurate application of feature-based similarity measures in a multi-ontology setting. Our proposal is evaluated with regard human judgements of similarity for several benchmarks and ontologies. Results shows an improvement against related works, with similarity accuracies that even rival those obtained in an ideal mono-ontology setting.