5 research outputs found
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
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
As an important variant of entity alignment (EA), multi-modal entity
alignment (MMEA) aims to discover identical entities across different knowledge
graphs (KGs) with relevant images attached. We noticed that current MMEA
algorithms all globally adopt the KG-level modality fusion strategies for
multi-modal entity representation but ignore the variation in modality
preferences for individual entities, hurting the robustness to potential noise
involved in modalities (e.g., blurry images and relations). In this paper, we
present MEAformer, a multi-modal entity alignment transformer approach for meta
modality hybrid, which dynamically predicts the mutual correlation coefficients
among modalities for entity-level feature aggregation. A modal-aware hard
entity replay strategy is further proposed for addressing vague entity details.
Experimental results show that our model not only achieves SOTA performance on
multiple training scenarios including supervised, unsupervised, iterative, and
low resource, but also has a comparable number of parameters, optimistic speed,
and good interpretability. Our code and data are available at
https://github.com/zjukg/MEAformer.Comment: Repository: https://github.com/zjukg/MEAforme
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