11,580 research outputs found
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Entity alignment is to find identical entities in different knowledge graphs
(KGs) that refer to the same real-world object. Embedding-based entity
alignment techniques have been drawing a lot of attention recently because they
can help solve the issue of symbolic heterogeneity in different KGs. However,
in this paper, we show that the progress made in the past was due to biased and
unchallenging evaluation. We highlight two major flaws in existing datasets
that favor embedding-based entity alignment techniques, i.e., the isomorphic
graph structures in relation triples and the weak heterogeneity in attribute
triples. Towards a critical evaluation of embedding-based entity alignment
methods, we construct a new dataset with heterogeneous relations and attributes
based on event-centric KGs. We conduct extensive experiments to evaluate
existing popular methods, and find that they fail to achieve promising
performance. As a new approach to this difficult problem, we propose a
time-aware literal encoder for entity alignment. The dataset and source code
are publicly available to foster future research. Our work calls for more
effective and practical embedding-based solutions to entity alignment.Comment: submitted to ISWC 202
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
Source-Aware Embedding Training on Heterogeneous Information Networks
Heterogeneous information networks (HINs) have been extensively applied to
real-world tasks, such as recommendation systems, social networks, and citation
networks. While existing HIN representation learning methods can effectively
learn the semantic and structural features in the network, little awareness was
given to the distribution discrepancy of subgraphs within a single HIN.
However, we find that ignoring such distribution discrepancy among subgraphs
from multiple sources would hinder the effectiveness of graph embedding
learning algorithms. This motivates us to propose SUMSHINE (Scalable
Unsupervised Multi-Source Heterogeneous Information Network Embedding) -- a
scalable unsupervised framework to align the embedding distributions among
multiple sources of an HIN. Experimental results on real-world datasets in a
variety of downstream tasks validate the performance of our method over the
state-of-the-art heterogeneous information network embedding algorithms.Comment: Published in Data Intelligence 202
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