Optimizing enterprise search by automatically relating user context to textual document content

  • Authors:

  • Matthias Reichhold;Jörg Kerschbaumer;Günther Fliedl

  • Affiliations:

  • Universität Klagenfurt, Universitätsstraße, Klagenfurt;Universität Klagenfurt, Universitätsstraße, Klagenfurt;Universität Klagenfurt, Universitätsstraße, Klagenfurt

  • Venue:
  • i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
  • Year:
  • 2011

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Abstract

It is widely agreed that information retrieval (IR) systems benefit enormously from considering not only the user's query but also contextual data. In enterprise IR systems corporate knowledge bases and additional manually triggered information about users are normally taken to obtain such contextual data. In this paper we propose a solution for role-specific search in enterprise environments without the need of manual administration of mappings between roles and documents. We include collaboratively constructed knowledge engineering systems for computing similarity measures between user role attributes and relevant information snippets in enterprise documents. Our approach suggestsoptimizing such enterprise search systems by a role-sensitive ranking algorithm that relates contextually-derived information needs of users to unstructured (textual) data in documents. Hence we introduce a linguistic conceptfor generatingrole describing word vectorsbased on query (search) histories and corporate knowledge base generation. The Introduction outlines some basic ideas concerning the major areas of enterprise search, some relevant differences between web search and enterprise search. Subsequently we sketch our optimized enterprise search model. In Chapter 2some theoretical background and Related Work is briefly discussed.Chapter 3depicts some linguistically relevant details of our proposed model. We discuss our concept of User Roles, Role Term Vectors, some approaches for Role Term Extraction andTerm Extraction incorporating knowledge bases and query histories. In Chapter4 we describe our ranking mechanism, the re-ranking strategy and the method for Role Relevance Scoring. Chapter 5 gives a conclusion of the work as well as an outlook on future work.