Inter-domain Multi-relational Link Prediction
ECML PKDD 2021. Lecture Notes in Computer Science, vol 12976 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 m...
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Zusammenfassung: | ECML PKDD 2021. Lecture Notes in Computer Science, vol 12976 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. |
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DOI: | 10.48550/arxiv.2106.06171 |