Search for expertise: going beyond direct evidence

Pavel Serdyukov

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

78 Downloads (Pure)

Abstract

The automatic search for knowledgeable people in the scope of an organization is a key function which makes modern Enterprise search systems commercially successful and socially demanded. A number of effective approaches to expert finding were recently proposed in academic publications. Although, most of them use reasonably defined measures of personal expertise, they often limit themselves to rather unrealistic and sometimes oversimplified principles. In this thesis, we explore several ways to go beyond state-of-the-art assumptions used in research on expert finding and propose several novel solutions for this and related tasks. First, we describe measures of expertise that do not assume independent occurrence of terms and persons in a document what makes them perform better than the measures based on independence of all entities in a document. One of these measures makes persons central to the process of terms generation in a document. Another one assumes that the position of the person's mention in a document with respect to the positions of query terms indicates the relation of the person to the document's relevant content. Second, we find the ways to use not only direct expertise evidence for a person concentrated within the document space of the person's current employer and only within those organizational documents that mention the person. We successfully utilize the predicting potential of additional indirect expertise evidence publicly available on the Web and in the organizational documents implicitly related to a person. Finally, besides the expert finding methods we proposed, we also demonstrate solutions for the tasks from related domains. In one case, we use several algorithms of multi-step relevance propagation to search for typed entities in Wikipedia. In another case, we suggest generic methods for placing photos uploaded to Flickr on the World map using language models of locations built entirely on the annotations provided by users with a few task specific extensions.
Original languageUndefined
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Hiemstra, Djoerd , Advisor
  • Apers, Peter Maria Gerardus, Supervisor
Thesis sponsors
Award date24 Jun 2009
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-2845-0
DOIs
Publication statusPublished - 24 Jun 2009

Keywords

  • METIS-265211
  • CR-H.3.3
  • IR-61651
  • EWI-15407

Cite this

Serdyukov, Pavel. / Search for expertise: going beyond direct evidence. Enschede : University of Twente, 2009. 158 p.
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Search for expertise: going beyond direct evidence. / Serdyukov, Pavel.

Enschede : University of Twente, 2009. 158 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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T1 - Search for expertise: going beyond direct evidence

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AB - The automatic search for knowledgeable people in the scope of an organization is a key function which makes modern Enterprise search systems commercially successful and socially demanded. A number of effective approaches to expert finding were recently proposed in academic publications. Although, most of them use reasonably defined measures of personal expertise, they often limit themselves to rather unrealistic and sometimes oversimplified principles. In this thesis, we explore several ways to go beyond state-of-the-art assumptions used in research on expert finding and propose several novel solutions for this and related tasks. First, we describe measures of expertise that do not assume independent occurrence of terms and persons in a document what makes them perform better than the measures based on independence of all entities in a document. One of these measures makes persons central to the process of terms generation in a document. Another one assumes that the position of the person's mention in a document with respect to the positions of query terms indicates the relation of the person to the document's relevant content. Second, we find the ways to use not only direct expertise evidence for a person concentrated within the document space of the person's current employer and only within those organizational documents that mention the person. We successfully utilize the predicting potential of additional indirect expertise evidence publicly available on the Web and in the organizational documents implicitly related to a person. Finally, besides the expert finding methods we proposed, we also demonstrate solutions for the tasks from related domains. In one case, we use several algorithms of multi-step relevance propagation to search for typed entities in Wikipedia. In another case, we suggest generic methods for placing photos uploaded to Flickr on the World map using language models of locations built entirely on the annotations provided by users with a few task specific extensions.

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Serdyukov P. Search for expertise: going beyond direct evidence. Enschede: University of Twente, 2009. 158 p. https://doi.org/10.3990/1.9789036528450