Semantic distance acquisition in SemaCS

Sjachyn, M. and Beus-Dukic, L. 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

Chapter titleSemantic distance acquisition in SemaCS
AuthorsSjachyn, M. and Beus-Dukic, L.
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.

Book titleProceedings of the 4th IEEE international conference on research challenges in information science (RCIS 2010), Nice, France.
Page range183-190
Year2010
PublisherIEEE
Publication dates
Published2010
ISBN9781424448395
Digital Object Identifier (DOI)doi:10.1109/RCIS.2010.5507378

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