Contextual correlates of synonymy
Communications of the ACM
Ontologies for conceptual modeling: their creation, use, and management
Data & Knowledge Engineering
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Topic distillation using hierarchy concept tree
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Towards the development of a conceptual distance metric for the UMLS
Journal of Biomedical Informatics
HLT '93 Proceedings of the workshop on Human Language Technology
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Inter-patient distance metrics using SNOMED CT defining relationships
Journal of Biomedical Informatics
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Context-based literature digital collection search
The VLDB Journal — The International Journal on Very Large Data Bases
Semi-structured document categorization with a semantic kernel
Pattern Recognition
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Domain Ontology Learning from the Web
Domain Ontology Learning from the Web
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
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Processing natural language without natural language processing
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Guest Editorial: Ontologies for clinical and translational research: Introduction
Journal of Biomedical Informatics
Ontology augmentation: combining semantic web and text resources
Proceedings of the sixth international conference on Knowledge capture
Journal of Biomedical Informatics
Harnessing different knowledge sources to measure semantic relatedness under a uniform model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts
Journal of Biomedical Informatics
Employing UMLS for generating hints in a tutoring system for medical problem-based learning
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Ontology-based semantic clustering
AI Communications
Semantically-grounded construction of centroids for datasets with textual attributes
Knowledge-Based Systems
A semantic similarity method based on information content exploiting multiple ontologies
Expert Systems with Applications: An International Journal
Semantic similarity estimation from multiple ontologies
Applied Intelligence
Ontology relation alignment based on attribute semantics
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
International Journal on Semantic Web & Information Systems
Engineering Applications of Artificial Intelligence
Journal of Biomedical Informatics
Computing term similarity by large probabilistic isA knowledge
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
An ontology-based similarity measure for biomedical data - Application to radiology reports
Journal of Biomedical Informatics
A text scanning mechanism simulating human reading process
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Towards the estimation of feature-based semantic similarity using multiple ontologies
Knowledge-Based Systems
Future Generation Computer Systems
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Proper understanding of textual data requires the exploitation and integration of unstructured and heterogeneous clinical sources, healthcare records or scientific literature, which are fundamental aspects in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing, classification and structuring of textual resources. In the past, several approaches for assessing word similarity by exploiting different knowledge sources (ontologies, thesauri, domain corpora, etc.) have been proposed. Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies (such as MeSH or SNOMED CT). In this paper, these approaches are introduced and analyzed in order to determine their advantages and limitations with respect to the considered knowledge bases. After that, a new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed. Using SNOMED CT as the input ontology, the accuracy of our proposal is evaluated and compared against other approaches according to a standard benchmark of manually ranked medical terms. The correlation between the results of the evaluated measures and the human experts' ratings shows that our proposal outperforms most of the previous measures avoiding, at the same time, some of their limitations.