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Improving Enterprise Wide Search in Large Engineering Multinationals: A Linguistic Comparison of the Structures of Internet-Search and Enterprise-Search Queries

  • David Edward JonesEmail author
  • Yifan Xie
  • Chris McMahon
  • Marting Dotter
  • Nicolas Chanchevrier
  • Ben Hicks
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 467)

Abstract

Understanding how users formulate search queries can allow the development of search engines that are tailored to the way users search and thus improve the knowledge discovery process, a key challenge for Product Lifecycle Management (PLM) systems.

This paper presents part-of-speech (POS) statistical analysis on two sets of ‘Top 500’ search query lists in order to compare Internet search with enterprise search with the aim of understanding how enterprise search queries differ from Internet search queries. The Internet queries were obtained from the keyword research company WordTracker.com and covers the month of January 2015. Enterprise search logs were obtained from a large multinational engineering organization and represent the first six months of 2014.

The results show enterprise search users are far more likely to search using nouns, with 97 % of queries containing at least one noun. This compares to 89 % for Internet users. 60 % of enterprise queries are single nouns compared to 38 % for Internet search users. In total, enterprise queries fell into 41 lexical classes (noun-noun/adjective-noun/etc.) whilst Internet search contained 95 classes. Of those 41 classes only 12 % contained no nouns, compared to 21 % for Internet search. 80 % of the enterprise search queries can be covered by just four Lexical classes compared to 15 for Internet search. 90 % coverage required 11 classes for enterprise and 44 classes for the Internet.

These findings appear to support existing literature in that they show a preference for enterprise searches for specific information using domain specific terms. This paper concludes by considering the implications of these findings for enterprise search systems and PLM in the context of a large engineering organization and in particular proposes two areas of future research.

Keywords

Knowledge management Enterprise search 

Notes

Acknowledgments

This research is funded via an EPSRC CASE AWARD, the Language of Collaborative Manufacturing (LOCM) Project (EPSRC grant reference EP/K014196/1) and the Airbus Group. The Authors would like to thank colleagues at Airbus and the University of Bristol for support and contribution.

References

  1. 1.
    Mukherjee, R., Mao, J.: Enterprise search: tough stuff. Queue 2(2), 36 (2004)CrossRefGoogle Scholar
  2. 2.
    Stocker, A. et al.: Is enterprise search useful at all?: lessons learned from studying user behavior. In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, ACM (2014)Google Scholar
  3. 3.
    Varma, V.: Use of ontologies for organizational knowledge management and knowledge management systems. In: Sharman, R., Kishore, R., Ramesh, R. (eds.) ontologies, pp. 21–47. Springe, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Hawking, D., et al.: Context in enterprise search and delivery. In: Proceedings of IRiX Workshop, ACM SIGIR (2005)Google Scholar
  5. 5.
    McMahon, C., et al.: Waypoint: an integrated search and retrieval system for engineering documents. J. Comput. Inf. Sci. Eng. 4(4), 329–338 (2004)CrossRefGoogle Scholar
  6. 6.
    Sacco, G.M.: Dynamic taxonomies: A model for large information bases. IEEE Trans. Knowl. Data Eng. 12(3), 468–479 (2000)CrossRefGoogle Scholar
  7. 7.
    Allan, J., Raghavan, H.: Using part-of-speech patterns to reduce query ambiguity. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2002)Google Scholar
  8. 8.
    Barr, C., Jones, R., Regelson, M.: The linguistic structure of English web-search queries. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2008)Google Scholar
  9. 9.
    Ganchev, K., et al.: Using search-logs to improve query tagging. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers vol. 2. Association for Computational Linguistics (2012)Google Scholar
  10. 10.
    Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2003)Google Scholar
  11. 11.
    Nakagawa, H., Mori, T.: A simple but powerful automatic term extraction method. In: COLING-02 on COMPUTERM 2002: Second International Workshop on Computational Terminology vol. 14. Association for Computational Linguistics (2002)Google Scholar
  12. 12.
    Redon, R., Larsson, A., Leblond, R., Longueville, B.: VIVACE context based search platform. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 397–410. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Jones, D.E., Nicolas,C., McMahon, C., Hicks, B.: A strategy for artefact-based information navigation in large engineering organisations (InPress). In: ICED15: The 20th International Conference on Engineering Design, Milan, Italy (2015)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • David Edward Jones
    • 1
    Email author
  • Yifan Xie
    • 2
  • Chris McMahon
    • 1
  • Marting Dotter
    • 2
  • Nicolas Chanchevrier
    • 2
  • Ben Hicks
    • 1
  1. 1.Department of Mechanical EngineeringUniversity of BristolBristolUK
  2. 2.Airbus GroupToulouseFrance

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