NDLI: Discriminative probabilistic models for expert search in heterogeneous information sources

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Discriminative probabilistic models for expert search in heterogeneous information sources

Content Provider	Springer Nature Link
Author	Fang, Yi Si, Luo Mathur, Aditya P.
Copyright Year	2010
Abstract	In many realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, different information sources need to receive different weights to reflect their degrees of importance. However, most previous studies in expert finding did not differentiate data sources, which may lead to unsatisfactory performance in the settings where the heterogeneity of data sources is present. In this paper, we investigate how to merge and weight heterogeneous knowledge sources in the context of expert finding. A relevance-based supervised learning framework is presented to learn the combination weights from training data. Beyond just learning a fixed combination strategy for all the queries and experts, we propose a series of discriminative probabilistic models which have increasing capability to associate the combination weights with specific experts and queries. In the last (and also the most sophisticated) proposed model, the combination weights depend on both expert classes and query topics, and these classes/topics are derived from expert and query features. Compared with expert and query independent combination methods, the proposed combination strategy can better adjust to different types of experts and queries. In consequence, the model yields much flexibility of combining data sources when dealing with a broad range of expertise areas and a large variation in experts. To the best of our knowledge, this is the first work that designs discriminative learning models to rank experts. Empirical studies on two real world faculty expertise testbeds demonstrate the effectiveness and robustness of the proposed discriminative learning models.
Starting Page	158
Ending Page	177
Page Count	20
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	14
Issue Number	2
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-08-21
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Expert finding Expert search Learning to rank Information Storage and Retrieval Data Structures, Cryptology and Information Theory Pattern Recognition Document Preparation and Text Processing Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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