Validation of Growing Knowledge Graphs by Abductive Text Evidences

Jianfeng Du, Jeff Z. Pan, Sylvia Wang, Kunxun Qi, Yuming Shen, Yu Deng

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

Abstract

This paper proposes a validation mechanism for newly added triples in a growing knowledge graph. Given a logical theory, a knowledge graph, a text corpus, and a new triple to be validated, this mechanism computes a sorted list of explanations for the new triple to facilitate the validation of it, where an explanation, called an abductive text evidence, is a set of pairs of the form (triple, window) where appending the set of triples on the left to the knowledge graph enforces entailment of the new triple under the logical theory, while every sentence window on the right which is contained in the text corpus explains to some degree why the triple on the left is true. From the angle of practice, a special class of abductive text evidences called TEP-based abductive text evidence is proposed, which is constructed from explanation patterns seen before in the knowledge graph. Accordingly, a method for computing the complete set of TEP-based abductive text evidences is proposed. Moreover, a method for sorting abductive text evidences based on distantly supervised learning is proposed. To evaluate the proposed validation mechanism, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets demonstrate the efficiency and effectiveness of the proposed mechanism.
state-of-the-art approaches.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Subtitle of host publicationThirty-First Conference on Innovative Applications of Artificial Intelligence The Ninth Symposium on Educational Advances in Artificial Intelligence - AAAI Technical Track: AI and the Web
PublisherAAAI Press
Pages2784-2791
Number of pages8
Volume33
Edition1
ISBN (Electronic)978-1-57735-809-1
DOIs
Publication statusPublished - 17 Jul 2019
EventThe Thirty-Third AAAI Conference on Artificial Intelligence - Hilton Hawaiian Village, Honolulu, Hawaii, United States
Duration: 27 Jan 20191 Feb 2019
https://aaai.org/Conferences/AAAI-19/

Publication series

Name
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThe Thirty-Third AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2019
CountryUnited States
CityHonolulu, Hawaii
Period27/01/191/02/19
Internet address

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