Inter-domain Multi-relational Link Prediction
| Autor: | Phuc, Luu Huu, Takeuchi, Koh, Okajima, Seiji, Tolmachev, Arseny, Takebayashi, Tomoyoshi, Maruhashi, Koji, Kashima, Hisashi |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | ECML PKDD 2021. Lecture Notes in Computer Science, vol 12976 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-86520-7_18 |
| Popis: | 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. Comment: Camera-ready version, ECML-PKDD 2021 |
| Databáze: | arXiv |
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