scholarly journals Bootstrapping Entity Alignment with Knowledge Graph Embedding

2018 ◽  
Author(s):  
Zequn Sun ◽  
Wei Hu ◽  
Qingheng Zhang ◽  
Yuzhong Qu
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
State Of The Art ◽  
Graph Embedding ◽  
Training Data ◽  
Knowledge Graph ◽  
Error Accumulation ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.

scholarly journals Multi-view Knowledge Graph Embedding for Entity Alignment

2019 ◽  
Author(s):  
Qingheng Zhang ◽  
Zequn Sun ◽  
Wei Hu ◽  
Muhao Chen ◽  
Lingbing Guo ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
State Of The Art ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Multiple Views ◽  
Combination Strategies ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Inference Methods

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

2019 ◽  
Vol 33 ◽  
pp. 7152-7159 ◽  
Author(s):  
Peifeng Wang ◽  
Jialong Han ◽  
Chenliang Li ◽  
Rong Pan
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Daily Basis ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Attention Network ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity’s neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rules- and network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired properties.

scholarly journals Entity Alignment between Knowledge Graphs Using Attribute Embeddings

2019 ◽  
Vol 33 ◽  
pp. 297-304 ◽  
Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Large Numbers ◽  
Proposed Model ◽  
Alignment Task ◽  
Transitivity Rule ◽  
Knowledge Graphs

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

scholarly journals A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

scholarly journals Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment

2020 ◽  
Author(s):  
Hao Nie ◽  
Xianpei Han ◽  
Le Sun ◽  
Chi Man Wong ◽  
Qiang Chen ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Global Structure ◽  
Convolutional Networks ◽  
Local Relation ◽  
Knowledge Graphs ◽  
Art Performance ◽  
Real World Datasets ◽  
Coarse To Fine

Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object. To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit the global KG structure. Afterward, the aligned entities are identified based on their distances. In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment. Specifically, a global structure and local semantics preserving network is proposed to learn entity representations in a coarse-to-fine manner. Experiments on several real-world datasets show that our method significantly outperforms other entity alignment approaches and achieves the new state-of-the-art performance.

HC-COVID

2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-25
Author(s):  
Ziyi Kou ◽  
Lanyu Shang ◽  
Yang Zhang ◽  
Dong Wang
Keyword(s):  
Social Media ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Detection Accuracy ◽  
Critical Problem ◽  
Real World Datasets ◽  
Collaborative Efforts ◽  
Different Levels

The proliferation of social media has promoted the spread of misinformation that raises many concerns in our society. This paper focuses on a critical problem of explainable COVID-19 misinformation detection that aims to accurately identify and explain misleading COVID-19 claims on social media. Motivated by the lack of COVID-19 relevant knowledge in existing solutions, we construct a novel crowdsource knowledge graph based approach to incorporate the COVID-19 knowledge facts by leveraging the collaborative efforts of expert and non-expert crowd workers. Two important challenges exist in developing our solution: i) how to effectively coordinate the crowd efforts from both expert and non-expert workers to generate the relevant knowledge facts for detecting COVID-19 misinformation; ii) How to leverage the knowledge facts from the constructed knowledge graph to accurately explain the detected COVID-19 misinformation. To address the above challenges, we develop HC-COVID, a hierarchical crowdsource knowledge graph based framework that explicitly models the COVID-19 knowledge facts contributed by crowd workers with different levels of expertise and accurately identifies the related knowledge facts to explain the detection results. We evaluate HC-COVID using two public real-world datasets on social media. Evaluation results demonstrate that HC-COVID significantly outperforms state-of-the-art baselines in terms of the detection accuracy of misleading COVID-19 claims and the quality of the explanations.

scholarly journals An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 83-90
Author(s):  
Qing Guo ◽  
Zhu Sun ◽  
Jie Zhang ◽  
Yin-Leng Theng
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Recurrent Neural Network ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
User Mobility ◽  
Location Recommendation ◽  
Meta Path ◽  
Real World Datasets

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

scholarly journals FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 602
Author(s):  
Hongming Zhu ◽  
Xiaowen Wang ◽  
Yizhi Jiang ◽  
Hongfei Fan ◽  
Bowen Du ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Linear Time ◽  
Knowledge Graph ◽  
Multiple Objects ◽  
Instance Matching ◽  
Distributed Framework ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Data Collections

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

scholarly journals Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

2019 ◽  
Author(s):  
Yuting Wu ◽  
Xiao Liu ◽  
Yansong Feng ◽  
Zheng Wang ◽  
Rui Yan ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Dual Graph ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Complex Relation ◽  
Dual Relation ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Heterogeneous Knowledge

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.

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