Hierarchical Language Models for Expert Finding in Enterprise Corpora

Petkova, Desislava; Croft, W. Bruce

doi:10.1109/ictai.2006.63

Cited by 76 publications

(89 citation statements)

References 8 publications

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“…For example, instead of capturing the associations at the document level, they may be estimated at the paragraph or snippet level [7,20,24]. Other extensions incorporate additional forms of evidence through the use of priors [14], document structure [33], hierarchical, organizational, and topical context and structure [6,23], and Web data [25].…”

Section: Related Workmentioning

confidence: 99%

“…The two models have been first formalized and extensively compared by Balog et al [5], and are called candidate and document models, or Model 1 and Model 2, respectively. Candidate-based approaches (also referred to as profile-based [14] or query-independent [23] methods) build a textual (usually term-based) representation of candidate experts, and rank them based on a query/topic, using traditional ad-hoc retrieval models. The other type of approach, document-based models (also referred to as query-dependent approaches [23]), first find documents which are relevant to the topic, and then locate the associated experts.…”

Section: Related Workmentioning

confidence: 99%

“…Candidate-based approaches (also referred to as profile-based [14] or query-independent [23] methods) build a textual (usually term-based) representation of candidate experts, and rank them based on a query/topic, using traditional ad-hoc retrieval models. The other type of approach, document-based models (also referred to as query-dependent approaches [23]), first find documents which are relevant to the topic, and then locate the associated experts. Nearly all systems that took part in the 2005-2008 editions of the Expert Finding task at TREC implemented (variations on) one of these two approaches.…”

Section: Related Workmentioning

confidence: 99%

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A User-Oriented Model for Expert Finding

Смирнова

Balog

2011

Lecture Notes in Computer Science

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Abstract. Expert finding addresses the problem of retrieving a ranked list of people who are knowledgeable on a given topic. Several models have been proposed to solve this task, but so far these have focused solely on returning the most knowledgeable people as experts on a particular topic. In this paper we argue that in a real-world organizational setting the notion of the "best expert" also depends on the individual user and her needs. We propose a user-oriented approach that balances two factors that influence the user's choice: time to contact an expert, and the knowledge value gained after. We use the distance between the user and an expert in a social network to estimate contact time, and consider various social graphs, based on organizational hierarchy, geographical location, and collaboration, as well as the combination of these. Using a realistic test set, created from interactions of employees with a university-wide expert search engine, we demonstrate substantial improvements over a state-of-the-art baseline on all retrieval measures.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A User-Oriented Model for Expert Finding

Смирнова

Balog

2011

Lecture Notes in Computer Science

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“…However, the latter approach looks similar to the classic query expansion from a document collection (Lavrenko and Croft, 2001), since most relevant documents in most relevant profiles will supposedly be among the top retrieved documents from the collection anyway. Query expansion from organizational documents is actually popular in expert finding research and appears in works of Petkova and Croft (2006) and Balog et al (2008b) …”

Section: Document and Profile-based Query Expansionmentioning

confidence: 99%

“…Later, they suggested using topical profiles and ranked candidate experts by the richness of their profiles in topics expressed in a query (Balog and de Rijke, 2007b). Petkova and Croft (2006) grouped documents by type/format and weighted the contribution of documents from each group to a candidate's profile.…”

Section: Profile-based Expert Findingmentioning

confidence: 99%

Search for expertise : going beyond direct evidence

Serdyukov

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Integrating multiple windows and document features for expert finding

Zhu

Song

Rüger

2009

J. Am. Soc. Inf. Sci.

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Expert finding is a key task in enterprise search and has recently attracted lots of attention from both research and industry communities. The Text REtrieval Conference 2 (TREC) has organized an expert search task for 2005, 2006, and 2007. Given a search topic, a prominent existing approach is to apply some information retrieval (IR) system to retrieve top ranking documents, which will then be used to derive associations between experts and the search topic based on co occurrences. However, we argue that expert finding is more sensitive to multiple document features that current expert finding systems insufficiently address, including: (1) multiple levels of associations between experts and search topics, (2) document internal structure and (3) document authority. We propose a novel approach which integrates the above three aspects as well as a query expansion technique in a two stage model for expert finding. A systematic evaluation is conducted on the TREC2006 and TREC2005 expert search collections to test the performance of our approach and the effects of different aspects of document features and query expansion. These experimental results show that query expansion can dramatically improve expert finding performance.Both document internal structure and our novel multiple window based approach for taking into account multiple levels of associations improve expert finding over the direct use of a number of well known IR models for both with and without query expansion. KeywordsExpert finding, enterprise search 1 Correspondence author.

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Hierarchical Language Models for Expert Finding in Enterprise Corpora

Abstract: Abstract

Cited by 76 publications

References 8 publications

A User-Oriented Model for Expert Finding

A User-Oriented Model for Expert Finding

Search for expertise : going beyond direct evidence

Integrating multiple windows and document features for expert finding

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