Graph-Based Knowledge Consolidation in Ontology Population

Pum Mo RYU  Myung-Gil JANG  Hyun-Ki KIM  So-Young PARK  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.9   pp.2139-2142
Publication Date: 2013/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2139
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
knowledge graph,  knowledge consolidation,  entity consolidation,  relation consolidation,  

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We propose a novel method for knowledge consolidation based on a knowledge graph as a next step in relation extraction from text. The knowledge consolidation method consists of entity consolidation and relation consolidation. During the entity consolidation process, identical entities are found and merged using both name similarity and relation similarity measures. In the relation consolidation process, incorrect relations are removed using cardinality properties, temporal information and relation weight in given graph structure. In our experiment, we could generate compact and clean knowledge graphs where number of entities and relations are reduced by 6.1% and by 17.4% respectively with increasing relation accuracy from 77.0% to 85.5%.