SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
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
Towards the development of a conceptual distance metric for the UMLS
Journal of Biomedical Informatics
Formulating context-dependent similarity functions
Proceedings of the 13th annual ACM international conference on Multimedia
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
Kinds of Contexts and their Impact on Semantic Similarity Measurement
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Introduction to Information Retrieval
Introduction to Information Retrieval
How Can the Term Compositionality Be Useful for Acquiring Elementary Semantic Relations?
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
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
Methodological Review: Empirical distributional semantics: Methods and biomedical applications
Journal of Biomedical Informatics
Mining a lexicon of technical terms and lay equivalents
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The effect of context on semantic similarity measurement
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
Data Mining in Biomedicine Using Ontologies
Data Mining in Biomedicine Using Ontologies
Section classification in clinical notes using supervised hidden markov model
Proceedings of the 1st ACM International Health Informatics Symposium
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Directional distributional similarity for lexical inference
Natural Language Engineering
An ontology-based measure to compute semantic similarity in biomedicine
Journal of Biomedical Informatics
Towards a framework for developing semantic relatedness reference standards
Journal of Biomedical Informatics
A context-aware semantic similarity model for ontology environments
Concurrency and Computation: Practice & Experience
An ontology-based similarity measure for biomedical data - Application to radiology reports
Journal of Biomedical Informatics
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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An open research question when leveraging ontological knowledge is when to treat different concepts separately from each other and when to aggregate them. For instance, concepts for the terms ''paroxysmal cough'' and ''nocturnal cough'' might be aggregated in a kidney disease study, but should be left separate in a pneumonia study. Determining whether two concepts are similar enough to be aggregated can help build better datasets for data mining purposes and avoid signal dilution. Quantifying the similarity among concepts is a difficult task, however, in part because such similarity is context-dependent. We propose a comprehensive method, which computes a similarity score for a concept pair by combining data-driven and ontology-driven knowledge. We demonstrate our method on concepts from SNOMED-CT and on a corpus of clinical notes of patients with chronic kidney disease. By combining information from usage patterns in clinical notes and from ontological structure, the method can prune out concepts that are simply related from those which are semantically similar. When evaluated against a list of concept pairs annotated for similarity, our method reaches an AUC (area under the curve) of 92%.