Authors:
Thu Le
1
;
Tuan-Dung Cao
2
;
Lam Pham
3
;
Trung Pham
3
and
Toan Luu
4
Affiliations:
1
Department of Research Methodology, Thuongmai University, Hanoi, Vietnam
;
2
School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
;
3
Department of Computer Science, School of Information Technology in Economics, National Economics University, Hanoi, Vietnam
;
4
Move Digital AG, Zurich, Switzerland
Keyword(s):
Topic Taxonomy Construction, Expert Finding System, Expertise Profile.
Abstract:
Although a lot of Expert finding systems have been proposed, there is a need for a comprehensive study on building a knowledge base of areas of expertise. Building an Ontology creates a consistent lexical framework of a domain for representing information, thus processing the data effectively. This study uses the background knowledge of machine learning methods and textual data mining techniques to build adaptive clustering, local embedding, and term ordering modules. By that means, it is possible to construct an Ontology for a domain via representation language and apply it to the Ontology system of expert information. We proposed a new method called TaxoGenDRK (Taxonomy Generator using Database about Research Area and Keyword) based on the method from Chao Zhang et al. (2018)’s research on TaxoGen and an additional module that uses a database of research areas and keywords retrieved from the internet – the data regarded as an uncertain knowledge base for learning about taxonomy. DB
LP dataset was used for testing, and the topic was “computer science”. The evaluation of the topic taxonomy using TaxogenDRK was implemented via qualitative and quantitative methods, producing a relatively good accuracy compared to other existing studies.
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