287 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
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)? A KG
is a multi-relational graph that has proven valuable for many tasks including
question answering and semantic search. In this paper, we present GENI, a
method for tackling the problem of estimating node importance in KGs, which
enables several downstream applications such as item recommendation and
resource allocation. While a number of approaches have been developed to
address this problem for general graphs, they do not fully utilize information
available in KGs, or lack flexibility needed to model complex relationship
between entities and their importance. To address these limitations, we explore
supervised machine learning algorithms. In particular, building upon recent
advancement of graph neural networks (GNNs), we develop GENI, a GNN-based
method designed to deal with distinctive challenges involved with predicting
node importance in KGs. Our method performs an aggregation of importance scores
instead of aggregating node embeddings via predicate-aware attention mechanism
and flexible centrality adjustment. In our evaluation of GENI and existing
methods on predicting node importance in real-world KGs with different
characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed,
and minor updates made in the Appendix (v2
Interactive Contrastive Learning for Self-supervised Entity Alignment
Self-supervised entity alignment (EA) aims to link equivalent entities across
different knowledge graphs (KGs) without seed alignments. The current SOTA
self-supervised EA method draws inspiration from contrastive learning,
originally designed in computer vision based on instance discrimination and
contrastive loss, and suffers from two shortcomings. Firstly, it puts
unidirectional emphasis on pushing sampled negative entities far away rather
than pulling positively aligned pairs close, as is done in the well-established
supervised EA. Secondly, KGs contain rich side information (e.g., entity
description), and how to effectively leverage those information has not been
adequately investigated in self-supervised EA. In this paper, we propose an
interactive contrastive learning model for self-supervised EA. The model
encodes not only structures and semantics of entities (including entity name,
entity description, and entity neighborhood), but also conducts cross-KG
contrastive learning by building pseudo-aligned entity pairs. Experimental
results show that our approach outperforms previous best self-supervised
results by a large margin (over 9% average improvement) and performs on par
with previous SOTA supervised counterparts, demonstrating the effectiveness of
the interactive contrastive learning for self-supervised EA.Comment: Accepted by CIKM 202
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment
Entity alignment(EA) is a crucial task for integrating cross-lingual and
cross-domain knowledge graphs(KGs), which aims to discover entities referring
to the same real-world object from different KGs. Most existing methods
generate aligning entity representation by mining the relevance of triple
elements via embedding-based methods, paying little attention to triple
indivisibility and entity role diversity. In this paper, a novel framework
named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware
Attention for Cross-lingual Entity Alignment is proposed to overcome the above
issues considering ensemble triple specificity and entity role features.
Specifically, the ensemble triple representation is derived by regarding
relation as information carrier between semantic space and type space, and
hence the noise influence during spatial transformation and information
propagation can be smoothly controlled via specificity-aware triple attention.
Moreover, our framework uses triple-ware entity enhancement to model the role
diversity of triple elements. Extensive experiments on three real-world
cross-lingual datasets demonstrate that our framework outperforms
state-of-the-art methods
How to Train Your Agent to Read and Write
Reading and writing research papers is one of the most privileged abilities
that a qualified researcher should master. However, it is difficult for new
researchers (\eg{students}) to fully {grasp} this ability. It would be
fascinating if we could train an intelligent agent to help people read and
summarize papers, and perhaps even discover and exploit the potential knowledge
clues to write novel papers. Although there have been existing works focusing
on summarizing (\emph{i.e.}, reading) the knowledge in a given text or
generating (\emph{i.e.}, writing) a text based on the given knowledge, the
ability of simultaneously reading and writing is still under development.
Typically, this requires an agent to fully understand the knowledge from the
given text materials and generate correct and fluent novel paragraphs, which is
very challenging in practice. In this paper, we propose a Deep ReAder-Writer
(DRAW) network, which consists of a \textit{Reader} that can extract knowledge
graphs (KGs) from input paragraphs and discover potential knowledge, a
graph-to-text \textit{Writer} that generates a novel paragraph, and a
\textit{Reviewer} that reviews the generated paragraph from three different
aspects. Extensive experiments show that our DRAW network outperforms
considered baselines and several state-of-the-art methods on AGENDA and
M-AGENDA datasets. Our code and supplementary are released at
https://github.com/menggehe/DRAW
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
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