Knowledge base refinement using limited amount of efforts from experts

Ki CHAN, Wai LAM, Tak Lam WONG

Research output: Contribution to journalArticle

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. Copyright © 2014 IGI Publishing Hershey.
Original languageEnglish
Pages (from-to)1-19
JournalInternational Journal of Knowledge-Based Organizations (IJKBO)
Volume4
Issue number2
DOIs
Publication statusPublished - 2014

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Labels
Decision making
Experiments
Problem-Based Learning

Citation

Chan, K., Lam, W., & Wong, T.-L. (2014). Knowledge base refinement using limited amount of efforts from experts. International Journal of Knowledge-Based Organizations (IJKBO), 4(2), 1-19.