Economics at your fingertips  

Knowledge Base Refinement Using Limited Amount of Efforts from Experts

Ki Chan, Wai Lam and Tak-Lam Wong
Additional contact information
Ki Chan: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China
Wai Lam: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China
Tak-Lam Wong: Department of Mathematics and Information Technology, The Hong Kong Institute of Education, Hong Kong, China

International Journal of Knowledge-Based Organizations (IJKBO), 2014, vol. 4, issue 2, 1-19

Abstract: Knowledge bases are essential for supporting decision making during intelligent information processing. Automatic construction of knowledge bases becomes infeasible without labeled data, a complete table of data records including answers to queries. Preparing such information requires huge efforts from experts. The authors propose a new knowledge base refinement framework based on pattern mining and active learning using an existing available knowledge base constructed from a different domain (source domain) solving the same task as well as some data collected from the target domain. The knowledge base investigated in this paper is represented by a model known as Markov Logic Networks. The authors' proposed method first analyzes the unlabeled target domain data and actively asks the expert to provide labels (or answers) a very small amount of automatically selected queries. The idea is to identify the target domain queries whose underlying relations are not sufficiently described by the existing source domain knowledge base. Potential relational patterns are discovered and new logic relations are constructed for the target domain by exploiting the limited amount of labeled target domain data and the unlabeled target domain data. The authors have conducted extensive experiments by applying our approach to two different text mining applications, namely, pronoun resolution and segmentation of citation records, demonstrating consistent improvements.

Date: 2014
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link) ... 018/ijkbo.2014040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

International Journal of Knowledge-Based Organizations (IJKBO) is currently edited by John Wang

More articles in International Journal of Knowledge-Based Organizations (IJKBO) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

Page updated 2018-05-16
Handle: RePEc:igg:jkbo00:v:4:y:2014:i:2:p:1-19