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Entity set expansion in knowledge graph: a heterogeneous information network perspective |
Chuan SHI1, Jiayu DING1, Xiaohuan CAO1, Linmei HU1( ), Bin WU1, Xiaoli LI2 |
1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore |
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Abstract Entity set expansion (ESE) aims to expand an entity seed set to obtain more entities which have common properties. ESE is important for many applications such as dictionary construction and query suggestion. Traditional ESE methods relied heavily on the text and Web information of entities. Recently, some ESE methods employed knowledge graphs (KGs) to extend entities. However, they failed to effectively and efficiently utilize the rich semantics contained in a KG and ignored the text information of entities in Wikipedia. In this paper, we model a KG as a heterogeneous information network (HIN) containing multiple types of objects and relations. Fine-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate entities. Then we rank the entities according to the meta path based structural similarity. Furthermore, to utilize the text description of entities in Wikipedia, we propose an extended model CoMeSE++ which combines both structural information revealed by a KG and text information in Wikipedia for ESE. Extensive experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.
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Keywords
entity set expansion
knowledge graph
heterogeneous information network
multi-type meta path
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Corresponding Author(s):
Linmei HU
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Just Accepted Date: 27 December 2019
Issue Date: 24 September 2020
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