Jointly Learning Entity and Relation Representations for Entity Alignment
Published 1 January 2019Open access
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao
Citations176
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper presents a novel joint learning framework for entity alignment that is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations and incorporates the relation approximation into entities to iteratively learn better representations for both.
Abstract
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Keywords
Computer ScienceDecision Sciences
Dagstuhl Research Online Publication ServerDetecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
16,170 Citations2025Chen, Tianyu, Wang, Zeyu +8 more
A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Translating embeddings for modeling multi-relational data
5,178 Citations2015Antoine Bordes, Nicolas Usunier +2 more
TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.
Lecture notes in computer scienceModeling Relational Data with Graph Convolutional Networks
5,060 Citations2018Michael Schlichtkrull, Thomas Kipf +4 more
It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
Journal of Computer-Aided Molecular DesignMolecular graph convolutions: moving beyond fingerprints
1,694 Citations2016Steven Kearnes, Kevin McCloskey +3 more
Molecular graph convolutions are described, a machine learning architecture for learning from undirected graphs, specifically small molecules, that represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
1,253 Citations2016Makoto Miwa, Mohit Bansal
A novel end-to-end neural model to extract entities and relations between them and compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8).
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
910 Citations2017Diego Marcheggiani, Ivan Titov
A version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs, is proposed, observing that GCN layers are complementary to LSTM ones.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
868 Citations2018Yuhao Zhang, Peng Qi +1 more
An extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel is proposed, and a novel pruning strategy is applied to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
610 Citations2018Zhichun Wang, Qingsong Lv +2 more
This paper proposes a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs) given a set of pre-aligned entities, and trains GCNs to embed entities of each language into a unified vector space.
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
540 Citations2017Muhao Chen, Yingtao Tian +2 more
MTransE, a translation-based model for multilingual knowledge graph embeddings, is proposed to provide a simple and automated solution to achieve cross-lingual knowledge alignment and explore how MTransE preserves the key properties of its monolingual counterpart.
Bootstrapping Entity Alignment with Knowledge Graph Embedding
531 Citations2018Zequn Sun, Wei Hu +2 more
A bootstrapping approach to embedding-based entity alignment that iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings and employs an alignment editing method to reduce error accumulation during iterations.
Lecture notes in computer scienceCross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding
404 Citations2017Zequn Sun, Wei Hu +1 more
The experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.
Proceedings of the AAAI Conference on Artificial IntelligenceVariational Reasoning for Question Answering With Knowledge Graph
382 Citations2018Yuyu Zhang, Hanjun Dai +3 more
This work proposes a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously.
Iterative Entity Alignment via Joint Knowledge Embeddings
379 Citations2017Hao Zhu, Ruobing Xie +2 more
This paper presents a novel approach for entity alignment via joint knowledge embeddings that jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities.
Proceedings of the VLDB EndowmentPARIS
365 Citations2011Fabian M. Suchanek, Serge Abiteboul +1 more
This work presents paris, an approach for the automatic alignment of ontologies, which aligns not only instances, but also relations and classes and provides a truly holistic solution to the problem of ontology alignment.
Lancaster EPrints (Lancaster University)Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
307 Citations2019Wu, Y, Liu, X +4 more
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
258 Citations2017Bishan Yang, Tom M. Mitchell
KBLSTM is proposed, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading and achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.
arXiv (Cornell University)Highway Networks
247 Citations2015Rupesh K. Srivastava, Klaus Greff +1 more
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
231 Citations2018Muhao Chen, Yingtao Tian +3 more
This paper introduces an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions and shows that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches.
Ghent University Academic Bibliography (Ghent University)Adversarial training for multi-context joint entity and relation extraction
192 Citations2018Bekoulis, Ioannis, Johannes Deleu +2 more
arXiv (Cornell University)Variational Reasoning for Question Answering with Knowledge Graph
180 Citations2017Yuyu Zhang, Hanjun Dai +3 more
SIGMa
149 Citations2013Simon Lacoste-Julien, Konstantina Palla +4 more
Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts, which is an iterative propagation algorithm that leverages both the structural information from the relationship graph and flexible similarity measures between entity properties in a greedy local search, which makes it scalable.
Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference
144 Citations2019Shichao Pei, Yu Lu +2 more
This paper proposes a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment, and improves the knowledge graph embedding with awareness of the degree difference by performing the adversarial training.
A self-training approach for resolving object coreference on the semantic web
117 Citations2011Wei Hu, Jianfeng Chen +1 more
This paper proposes a self-training approach for object coreference resolution on the Semantic Web, which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates.
Communications in computer and information scienceA Joint Embedding Method for Entity Alignment of Knowledge Bases
104 Citations2016Yanchao Hao, Yuanzhe Zhang +3 more
A model which jointly learns the embeddings of multiple knowledge bases (KBs) in a uniform vector space to align entities in KBs and shows that the proposed approach which only utilize the structure information of KBs also works well.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
82 Citations2018Guanying Wang, Wen Zhang +6 more
A label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
arXiv (Cornell University)PARIS: Probabilistic Alignment of Relations, Instances, and Schema
65 Citations2011Fabian M. Suchanek, Serge Abiteboul +1 more
Proceedings of the VLDB EndowmentMultilingual schema matching for Wikipedia infoboxes
38 Citations2011Thanh Nguyen, Viviane P. Moreira +3 more
This paper proposes a method for identifying mappings between attributes from infoboxes that come from pages in different languages in a completely automated fashion and demonstrates that the multilingual mappings it derives lead to substantial improvements in answer quality and coverage for structured queries over Wikipedia content.
Entity Matching across Heterogeneous Sources
33 Citations2015Yang Yang, Yizhou Sun +3 more
A probabilistic topic model is proposed that can not only find the matched documents for a query entity, but also explain why these documents are related by showing the common topics they share.
arXiv (Cornell University)SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
26 Citations2012Simon Lacoste-Julien, Konstantina Palla +4 more
Non-translational Alignment for Multi-relational Networks
21 Citations2018Shengnan Li, Xin Li +4 more
A non-translational approach, which aims to utilize a probabilistic model to offer more robust solutions to the alignment task, by exploring the structural properties as well as leveraging on anchors to project each network onto the same vector space during the process of learning the representation of individual networks.