1,691 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
Visual Pivoting for (Unsupervised) Entity Alignment
This work studies the use of visual semantic representations to align
entities in heterogeneous knowledge graphs (KGs). Images are natural components
of many existing KGs. By combining visual knowledge with other auxiliary
information, we show that the proposed new approach, EVA, creates a holistic
entity representation that provides strong signals for cross-graph entity
alignment. Besides, previous entity alignment methods require human labelled
seed alignment, restricting availability. EVA provides a completely
unsupervised solution by leveraging the visual similarity of entities to create
an initial seed dictionary (visual pivots). Experiments on benchmark data sets
DBP15k and DWY15k show that EVA offers state-of-the-art performance on both
monolingual and cross-lingual entity alignment tasks. Furthermore, we discover
that images are particularly useful to align long-tail KG entities, which
inherently lack the structural contexts necessary for capturing the
correspondences.Comment: To appear at AAAI-202
OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
Cross-lingual and cross-domain knowledge alignment without sufficient
external resources is a fundamental and crucial task for fusing irregular data.
As the element-wise fusion process aiming to discover equivalent objects from
different knowledge graphs (KGs), entity alignment (EA) has been attracting
great interest from industry and academic research recent years. Most of
existing EA methods usually explore the correlation between entities and
relations through neighbor nodes, structural information and external
resources. However, the complex intrinsic interactions among triple elements
and role information are rarely modeled in these methods, which may lead to the
inadequate illustration for triple. In addition, external resources are usually
unavailable in some scenarios especially cross-lingual and cross-domain
applications, which reflects the little scalability of these methods. To tackle
the above insufficiency, a novel universal EA framework (OTIEA) based on
ontology pair and role enhancement mechanism via triple-aware attention is
proposed in this paper without introducing external resources. Specifically, an
ontology-enhanced triple encoder is designed via mining intrinsic correlations
and ontology pair information instead of independent elements. In addition, the
EA-oriented representations can be obtained in triple-aware entity decoder by
fusing role diversity. Finally, a bidirectional iterative alignment strategy is
deployed to expand seed entity pairs. The experimental results on three
real-world datasets show that our framework achieves a competitive performance
compared with baselines
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
Entity alignment (EA) aims to find entities in different knowledge graphs
(KGs) that refer to the same object in the real world. Recent studies
incorporate temporal information to augment the representations of KGs. The
existing methods for EA between temporal KGs (TKGs) utilize a time-aware
attention mechanism to incorporate relational and temporal information into
entity embeddings. The approaches outperform the previous methods by using
temporal information. However, we believe that it is not necessary to learn the
embeddings of temporal information in KGs since most TKGs have uniform temporal
representations. Therefore, we propose a simple graph neural network (GNN)
model combined with a temporal information matching mechanism, which achieves
better performance with less time and fewer parameters. Furthermore, since
alignment seeds are difficult to label in real-world applications, we also
propose a method to generate unsupervised alignment seeds via the temporal
information of TKG. Extensive experiments on public datasets indicate that our
supervised method significantly outperforms the previous methods and the
unsupervised one has competitive performance.Comment: Accepted by COLING 202
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