10,587 research outputs found
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in
knowledge engineering. Existing EA methods mostly focus on utilizing the graph
structures and entity attributes (including literals), but ignore images that
are common in modern multi-modal KGs. In this study we first constructed
Multi-OpenEA -- eight large-scale, image-equipped EA benchmarks, and then
evaluated some existing embedding-based methods for utilizing images. In view
of the complementary nature of visual modal information and logical deduction,
we further developed a new multi-modal EA method named LODEME using logical
deduction and multi-modal KG embedding, with state-of-the-art performance
achieved on Multi-OpenEA and other existing multi-modal EA benchmarks.Comment: Accepted by ICASSP202
Neighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge
for entity alignment. This paper presents Neighborhood Matching Network (NMN),
a novel entity alignment framework for tackling the structural heterogeneity
challenge. NMN estimates the similarities between entities to capture both the
topological structure and the neighborhood difference. It provides two
innovative components for better learning representations for entity alignment.
It first uses a novel graph sampling method to distill a discriminative
neighborhood for each entity. It then adopts a cross-graph neighborhood
matching module to jointly encode the neighborhood difference for a given
entity pair. Such strategies allow NMN to effectively construct
matching-oriented entity representations while ignoring noisy neighbors that
have a negative impact on the alignment task. Extensive experiments performed
on three entity alignment datasets show that NMN can well estimate the
neighborhood similarity in more tough cases and significantly outperforms 12
previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202
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