A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms

Authors

  • Kyle Goslin Technological University Dublin
  • Markus Hofmann Technological University Dublin

DOI:

https://doi.org/10.4995/jclr.2019.11159

Keywords:

Automatic Search Query Enhancement, Text Analysis, Wikipedia

Abstract

Automatic Search Query Enhancement (ASQE) is the process of modifying a user submitted search query and identifying terms that can be added or removed to enhance the relevance of documents retrieved from a search engine. ASQE differs from other enhancement approaches as no human interaction is required. ASQE algorithms typically rely on a source of a priori knowledge to aid the process of identifying relevant enhancement terms. This paper describes the results of a qualitative analysis of the enhancement terms generated by the Wikipedia NSubstate Algorithm (WNSSA) for ASQE. The WNSSA utilises Wikipedia as the sole source of a priori knowledge during the query enhancement process. As each Wikipedia article typically represents a single topic, during the enhancement process of the WNSSA, a mapping is performed between the user’s original search query and Wikipedia articles relevant to the query. If this mapping is performed correctly, a collection of potentially relevant terms and acronyms are accessible for ASQE. This paper reviews the results of a qualitative analysis process performed for the individual enhancement term generated for each of the 50 test topics from the TREC-9 Web Topic collection. The contributions of this paper include: (a) a qualitative analysis of generated WNSSA search query enhancement terms and (b) an analysis of the concepts represented in the TREC-9 Web Topics, detailing interpretation issues during query-to-Wikipedia article mapping performed by the WNSSA.

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Author Biographies

Kyle Goslin, Technological University Dublin

Lecturer in Computing, Department of Informatics andEngineering

Markus Hofmann, Technological University Dublin

Department of Informatics andEngineering

References

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Published

2019-07-16

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Articles