Knowledge fragment enrichment using domain knowledge base

Jing Zhang, Honglei Zhuang, Yanglei Song, Jiawei Han, Yutao Zhang, Jie Tang, Juanzi Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Knowledge fragment enrichment aims to complete user input concept fragment by augmenting each concept with rich domain information. This is a widely studied problem in cognitive science, but has not been intensively investigated in computer science. In this paper, we formally define the problem of knowledge fragment enrichment in domain knowledge base and develop a probabilistic graphical model to tackle the problem. The proposed model is able to model the dependencies among concepts in the input knowledge fragment and also capture the probabilistic relationship between concepts and domain entities. We empirically evaluate the proposed model on two different genres of datasets: PubMed and NSFC. On both datasets, the proposed model significantly improves the accuracy of label prediction task by up to 3–9% (in terms of MAP) compared with several alternative enrichment methods.

Original languageEnglish (US)
Title of host publicationSocial Media Processing - 5th National Conference, SMP 2016, Proceedings
EditorsHongfei Lin, Yuming Li, Guoxiong Xiang, Mingwen Wang
PublisherSpringer-Verlag Berlin Heidelberg
Pages274-286
Number of pages13
ISBN (Print)9789811029929
DOIs
StatePublished - 2016
Event5th National Conference on Social Media Processing, SMP 2016 - Nanchang, China
Duration: Oct 29 2016Oct 30 2016

Publication series

NameCommunications in Computer and Information Science
Volume669
ISSN (Print)1865-0929

Other

Other5th National Conference on Social Media Processing, SMP 2016
CountryChina
CityNanchang
Period10/29/1610/30/16

Keywords

  • Heterogeneous information networks
  • Hierarchical topic modeling
  • Semi-supervised labeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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