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Number 1

Improving Enterprise Search in the Upstream Oil and Gas Industry by Automatic Query Expansion using a Non-probabilistic Knowledge Representation

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  • International Journal of Applied Information Systems
  • Foundation of Computer Science (FCS), NY, USA
  • Volume 1 - Number 1
  • Year of Publication: 2012
  • Authors: Paul H. Cleverley
  • 10.5120/ijais12-450723
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  • Paul H. Cleverley 2012. Improving Enterprise Search in the Upstream Oil and Gas Industry by Automatic Query Expansion using a Non-probabilistic Knowledge Representation. International Journal of Applied Information Systems. 1, 1 (November 2012), 25-32. DOI=http://dx.doi.org/10.5120/ijais450723
  • @article{10.5120/ijais2017451568,
    author = {Paul H. Cleverley},
    title = {Improving Enterprise Search in the Upstream Oil and Gas Industry by Automatic Query Expansion using a Non-probabilistic Knowledge Representation},
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    issue_date = {November 2012},
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    year = {2012},
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    pages = {25-32},
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    %J International Journal of Applied Information Systems
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Abstract

Organizations face a vocabulary disconnect between the terminology people use in search and the inherent ambiguity of terminology in their information. The mismatch leads to critical information being missed. This paper discusses how Boolean keyword search, the most commonly used approach in Enterprise search, compares with automatic Query Expansion (QE) using a non-probabilistic Knowledge Representation (KR) created independently of the corpus.

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Keywords

Enterprise search, Web Digital Library, Query Expansion, Knowledge Representation, Information Retrieval, Semantic Ambiguity, Taxonomy, Ontology, Combinatorial Linguistic Explosion, Petroleum Exploration and Production (E&P)

Index Terms

Computer Science
Information Sciences