1,195 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
Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Existing entity alignment methods mainly vary on the choices of encoding the
knowledge graph, but they typically use the same decoding method, which
independently chooses the local optimal match for each source entity. This
decoding method may not only cause the "many-to-one" problem but also neglect
the coordinated nature of this task, that is, each alignment decision may
highly correlate to the other decisions. In this paper, we introduce two
coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and
joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first
retrieves the model-confident alignments from the predicted results and then
incorporates them as additional knowledge to resolve the remaining
model-uncertain alignments. To achieve this, we further propose an enhanced
alignment model that is built on the current state-of-the-art baseline. In
addition, to address the many-to-one problem, we propose to jointly predict
entity alignments so that the one-to-one constraint can be naturally
incorporated into the alignment prediction. Experimental results show that our
model achieves the state-of-the-art performance and our reasoning methods can
also significantly improve existing baselines.Comment: in AAAI 202
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Graph neural networks (GNNs) have emerged as a powerful paradigm for
embedding-based entity alignment due to their capability of identifying
isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart
entities usually have non-isomorphic neighborhood structures, which easily
causes GNNs to yield different representations for them. To tackle this
problem, we propose a new KG alignment network, namely AliNet, aiming at
mitigating the non-isomorphism of neighborhood structures in an end-to-end
manner. As the direct neighbors of counterpart entities are usually dissimilar
due to the schema heterogeneity, AliNet introduces distant neighbors to expand
the overlap between their neighborhood structures. It employs an attention
mechanism to highlight helpful distant neighbors and reduce noises. Then, it
controls the aggregation of both direct and distant neighborhood information
using a gating mechanism. We further propose a relation loss to refine entity
representations. We perform thorough experiments with detailed ablation studies
and analyses on five entity alignment datasets, demonstrating the effectiveness
of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
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