Joint Multilingual Knowledge Graph Completion and Alignment
Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc, Viet Hung Nguyen, Mathias Niepert

TL;DR
This paper introduces a joint model for multilingual knowledge graph completion and alignment that leverages relation-aware graph neural networks to improve both tasks simultaneously, outperforming existing methods on benchmark datasets.
Contribution
The paper presents a novel joint model combining KG completion and alignment using relation-aware GNNs, structural inconsistency reduction, and seed enlargement mechanisms.
Findings
Outperforms existing baselines on multilingual KG tasks
Achieves state-of-the-art results on MKGC and MKGA benchmarks
Demonstrates the effectiveness of joint modeling for KG tasks
Abstract
Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs, such as alignments between multilingual KGs with common entities and relations, a deeper understanding of the ways in which multilingual KG completion (MKGC) can aid the creation of multilingual KG alignments (MKGA) is still limited. Motivated by the observation that structural inconsistencies -- the main challenge for MKGA models -- can be mitigated through KG completion methods, we propose a novel model for jointly completing and aligning knowledge graphs. The proposed model combines two components that jointly accomplish KG completion and alignment. These two components employ relation-aware graph neural networks that we propose to encode multi-hop neighborhood structures into entity and relation representations.…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Semantic Web and Ontologies
