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Semantic distance acquisition in SemaCS

Sjachyn, Maxym and Beus-Dukic, Ljerka (2010) Semantic distance acquisition in SemaCS. In: Proceedings of the 4th IEEE international conference on research challenges in information science (RCIS 2010), Nice, France. IEEE, pp. 183-190. ISBN 9781424448395

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Official URL: http://dx.doi.org/10.1109/RCIS.2010.5507378

Abstract

Search functionality and technology is a growing area of research. However, simple search approaches are still frequently used. A simple keyword or thesauri-based search is efficient and can be easily scaled. However, keyword-based search cannot be used to infer what may or may not be relevant to the user and thesauri, or any other expert generated model, is expensive to produce and tends to be of limited applicability. Semantic Component Selection (SemaCS) approach is not tied to any specific domain and does not rely on expert input. SemaCS is based on actual data and statistical semantic distances between words. Information on semantic distances is used for searching and for automated generation of domain model taxonomy. This paper presents SemaCS's means of acquiring these semantic distances - mNGD (2) - and its initial evaluation.

Item Type:Book Section
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:11145
Deposited On:11 Sep 2012 15:40
Last Modified:11 Sep 2012 15:40

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