Name
Abstract | ||
|---|---|---|
Knowledge graph entity typing, which is an important way to complete knowledge graphs (KGs), aims at predicting the associating type of certain given entities without any external knowledge. However, previous methods suppose that many (entity, entity type) pairs (ETPs) can be obtained for each entity type, performing poorly on entity types that only have a few associative entities and do not fully utilize the internal information in KGs. In this work, we propose a novel model named Meta Entity Typing (MET) for few-shot knowledge graph entity typing. In MET, we achieve knowledge graph entity typing by meta-learning with three sub-tasks formed by the hierarchical entity type tree in its meta-training stage. In this way, MET can focus on transferring type-specific meta information to learn the most important knowledge for entity typing. Besides, to fully employ the internal information in KGs given limited ETPs, inspired by Factorization Machines, we design a novel Relation To Relation Graph Convolutional Networks (R2R-GCN), in which we consider different relation combinations could have distinct influence on its corresponding entity, R2R-GCN can explicitly model the interactions between different relations. Empirically, our model achieves state-of-the-art results on few-shot entity typing KG benchmarks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-05933-9_26 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2022, PT I |
Keywords | DocType | Volume |
Knowledge graph entity typing, Few-shot learning, Graph convolutional networks | Conference | 13280 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guozhen Zhu | 1 | 0 | 0.34 |
| Zhongbao Zhang | 2 | 404 | 27.60 |
| Sen Su | 3 | 666 | 65.68 |