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A framework for unifying ontology-based semantic similarity measures

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TLDR
This paper presents a unifying framework that aims to improve the understanding of semantic measures, to highlight their equivalences and to propose bridges between their theoretical bases, and unify a large number of state-of-the-art semantic similarity measures through common expressions.
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This article is published in Journal of Biomedical Informatics.The article was published on 2014-04-01 and is currently open access. It has received 143 citations till now. The article focuses on the topics: Semantic similarity & Semantic computing.

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Semantic Similarity from Natural Language and Ontology Analysis

TL;DR: Semantic measures as discussed by the authors assess the similarity or relatedness of semantic entities by taking into account their semantics, i.e. their meaning; intuitively, the words tea and coffee, which both refer to stimulating beverages, will be estimated to be more semantically similar than the words toffee (confection) and coffee despite that the last pair has a higher syntactic similarity.
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BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.

TL;DR: This work proposes several approaches for sentence‐level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus.
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A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

TL;DR: This paper proposes a fuzzy ontology-based CBR framework that combines a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types and achieves an accuracy of 97.67%.
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Semantic Measures for the Comparison of Units of Language, Concepts or Entities from Text and Knowledge Base Analysis

TL;DR: This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses, which generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance.
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CirGO: an alternative circular way of visualising gene ontology terms.

TL;DR: This paper presents an open source CirGO (Circular Gene Ontology) software that visualises non-redundant two-level hierarchically structured ontology terms from gene expression data in a 2D space in an informative, comprehensive and intuitive format.
References
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Journal ArticleDOI

A translation approach to portable ontology specifications

TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.
Journal ArticleDOI

Features of Similarity

Amos Tversky
- 01 Jul 1977 - 
TL;DR: The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds and a set of qualitative assumptions are shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features.
Journal ArticleDOI

Linked Data - the story so far

TL;DR: The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.
Proceedings Article

An Information-Theoretic Definition of Similarity

Dekang Lin
TL;DR: This work presents an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model and demonstrates how this definition can be used to measure the similarity in a number of different domains.
Posted Content

Using Information Content to Evaluate Semantic Similarity in a Taxonomy

TL;DR: In this article, a new measure of semantic similarity in an IS-A taxonomy based on the notion of information content is presented, and experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r < 0.90 for human subjects performing the same task).