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The state of the art in semantic relatedness: a framework for comparison

Published online by Cambridge University Press:  27 March 2017

Yue Feng ,
Ebrahim Bagheri ,
Faezeh Ensan  and
Jelena Jovanovic
Yue Feng
Affiliation:
Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, M5B 2K3 ON, Canada e-mail: [email protected]
Ebrahim Bagheri
Affiliation:
Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, M5B 2K3 ON, Canada e-mail: [email protected]
Faezeh Ensan
Affiliation:
Department of Computer Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad, Razavi Khorasane-mail: [email protected]
Jelena Jovanovic
Affiliation:
Department of Software Engineering, School of Business Administration, University of Belgrade, Jove Ilica 154, 11000 Belgrade, Serbiae-mail: [email protected]
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Abstract

Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e10
Copyright
© Cambridge University Press, 2017 

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