Abstract—As numbers of high-quality, large volume
knowledge graphs appear, information extraction work has
been enriched with more semantic knowledge. However, the
entity relation extraction based on the knowledge graph is still
at a very intuitive early stage, and the key issue it faces is the
relation recognition and classification. In order to break the
shackles of ontology and describe the relationship between the
entities with fine-grained types, we propose a two-step
bottom-up abstraction approach for relation conceptualization
based on conceptual taxonomy that is automatically constructed.
Given an entity relation, we figure out a group of Top-K concept
pairs to abstract the relation, according to the typicality,
diversity and coverage features. Our experimental evaluation
shows that our method performing significantly high precision
and quality for detecting fine-grained relationships.
Index Terms—Knowledge graph, entity relationship, conceptualization, clustering.
Yang Yu, Youlang Ji, Jun Zhu, Hongying Zhao are with the Jiangsu Electric Power Company, GaoYou County Electric Power Supply, Company Gaoyou 225600, China (e-mail: email@example.com).
Jingjing Gu is with the Jiangsu Electric Power Company, Jurong County Electric Power Supply Company, Jurong 212400, China.
Cite: Yang Yu, Youlang Ji, Jun Zhu, Hongying Zhao, and Jingjing Gu, "Conceptualization of Entity Relationship Based on Knowledge Graph," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 466-471, 2018.