Neighborhood Matching Network for Entity Alignment
- URL: http://arxiv.org/abs/2005.05607v1
- Date: Tue, 12 May 2020 08:26:15 GMT
- Title: Neighborhood Matching Network for Entity Alignment
- Authors: Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang and Dongyan Zhao
- Abstract summary: Neighborhood Matching Network (NMN) is a novel entity alignment framework.
NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference.
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.
- Score: 71.24217694278616
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning [19.667806439200792]
We develop an innovative structure named (k,t)-ring neighborhood, where nodes are initially organized by their distance, forming different non-overlapping k-ring neighborhoods for each distance.
Within each k-ring structure, nodes are further categorized into different groups according to their types, thus emphasizing the heterogeneity of both distances and types in HINs naturally.
Experiments are conducted on downstream tasks to verify HHGT's superiority over 14 baselines, with a notable improvement of up to 24.75% in NMI and 29.25% in ARI for node clustering task.
arXiv Detail & Related papers (2024-07-18T04:58:27Z) - Entity Alignment with Unlabeled Dangling Cases [49.86384156476041]
We propose a novel GNN-based dangling detection and entity alignment framework.
While the two tasks share the same GNN, the detected dangling entities are removed in the alignment.
Our framework is featured by a designed entity and relation attention mechanism for selective neighborhood aggregation in representation learning.
arXiv Detail & Related papers (2024-03-16T17:21:58Z) - VN Network: Embedding Newly Emerging Entities with Virtual Neighbors [59.906332784508706]
We propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges.
First, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules.
Secondly, we identify both logic and symmetric path rules to capture complex patterns.
arXiv Detail & Related papers (2024-02-21T03:04:34Z) - EventEA: Benchmarking Entity Alignment for Event-centric Knowledge
Graphs [17.27027602556303]
We show that the progress made in the past was due to biased and unchallenging evaluation.
We construct a new dataset with heterogeneous relations and attributes based on event-centric KGs.
As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment.
arXiv Detail & Related papers (2022-11-05T05:34:21Z) - Optimizing Bi-Encoder for Named Entity Recognition via Contrastive
Learning [80.36076044023581]
We present an efficient bi-encoder framework for named entity recognition (NER)
We frame NER as a metric learning problem that maximizes the similarity between the vector representations of an entity mention and its type.
A major challenge to this bi-encoder formulation for NER lies in separating non-entity spans from entity mentions.
arXiv Detail & Related papers (2022-08-30T23:19:04Z) - Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning
and Embedding [29.81122170002021]
We propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives.
First, we propose a centrality-based subgraph generation algorithm to recall some landmark entities serving as the bridges between different subgraphs.
Second, we introduce self-supervised entity reconstruction to recover entity representations from incomplete neighborhood subgraphs.
Third, during the inference process, we merge the embeddings of subgraphs to make a single space for alignment search.
arXiv Detail & Related papers (2022-08-23T07:09:59Z) - Interpolation-based Correlation Reduction Network for Semi-Supervised
Graph Learning [49.94816548023729]
We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN)
In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries.
By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
arXiv Detail & Related papers (2022-06-06T14:26:34Z) - Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural
Networks [68.9026534589483]
RioGNN is a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture.
RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation.
arXiv Detail & Related papers (2021-04-16T04:30:06Z) - Nested Named Entity Recognition with Partially-Observed TreeCRFs [23.992944831013013]
We view nested NER as constituency parsing with partially-observed trees and model it with partially-observed TreeCRFs.
Our approach achieves the state-of-the-art (SOTA) F1 scores on the ACE2004, ACE2005 dataset, and shows comparable performance to SOTA models on the GENIA dataset.
arXiv Detail & Related papers (2020-12-15T18:20:36Z) - Relation-Aware Neighborhood Matching Model for Entity Alignment [8.098825914119693]
We propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment.
We show that the proposed model RNM performs better than state-of-the-art methods.
arXiv Detail & Related papers (2020-12-15T07:22:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.