Name
Abstract | ||
|---|---|---|
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. When related graphs coexist, it is of great benefit to build a larger graph via integrating the smaller ones. The integration requires predicting hidden relational connections between entities belonged to different graphs (inter-domain link prediction). However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities of the same graph only (intra-domain link prediction). In this study, we propose a new approach to tackle the inter-domain link prediction problem by softly aligning the entity distributions between different domains with optimal transport and maximum mean discrepancy regularizers. Experiments on real-world datasets show that optimal transport regularizer is beneficial and considerably improves the performance of baseline methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86520-7_18 | MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: RESEARCH TRACK, PT II |
Keywords | DocType | Volume |
Inter-domain link prediction, Multi-relational data, Optimal transport | Conference | 12976 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luu Huu Phuc | 1 | 0 | 0.68 |
| Koh Takeuchi | 2 | 59 | 11.29 |
| Seiji Okajima | 3 | 0 | 0.34 |
| Arseny Tolmachev | 4 | 0 | 1.35 |
| Tomoyoshi Takebayashi | 5 | 0 | 0.34 |
| Koji Maruhashi | 6 | 0 | 1.35 |
| Hisashi Kashima | 7 | 1739 | 118.04 |