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检索条件"主题词=Knowledge Graph embedding"

共 566 条记录，以下是181-190 订阅

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Two-Stage Entity Alignment: Combining Hybrid knowledge graph embedding with Similarity-Based Relation Alignment 16th

Two-Stage Entity Alignment: Combining Hybrid Knowledge Graph...

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16th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Jiang, Tingting Bu, Chenyang Zhu, Yi Wu, Xindong Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Hefei Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing Peoples R China

ISBN: (纸本)9783030299088;9783030299071

Entity alignment aims to automatically determine whether an entity pair in different knowledge graphs refers to the same entity in reality. Existing entity alignment methods can be classified into two categories: string-similarity-based methods and embedding-based methods. String-similarity-based methods have higher accuracy, however, they might have difficulty in dealing with literal heterogeneity, i.e., an entity pair in diverse forms. Though embedding-based entity alignment can deal with literal heterogeneity, they also suffer the shortcomings of higher time complexity and lower accuracy. Moreover, there remain limitations and challenges due to only using the structure information of triples for existing embedding methods. Therefore, in this study, we propose a two-stage entity alignment framework, which can combine the advantages of both methods. In addition, to enhance the embedding performance, a hybrid knowledge graph embedding model with both fact triples and logical rules is introduced for entity alignment. Experimental results on two real-world datasets show that the proposed method is significantly better than the state-of-the-art embedding-based entity alignment methods.

关键词： Entity alignment knowledge graph embedding Relation alignment

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Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for knowledge graph embedding 20

Rotate3D: Representing Relations as Rotations in Three-Dimen...

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29th ACM International Conference on Information and knowledge Management (CIKM)

作者： Gao, Chang Sun, Chengjie Shan, Lili Lin, Lei Wang, Mingjiang Harbin Inst Technol Harbin Heilongjiang Peoples R China Harbin Inst Technol SZ Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450368599

knowledge graph embedding, which aims to learn low-dimensional embeddings of entities and relations, plays a vital role in a wide range of applications. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry, inversion, and composition. However, most existing methods fail to model the non-commutative composition pattern, which is essential, especially for multi-hop reasoning. To address this issue, we propose a new model called Rotate3D, which maps entities to the three-dimensional space and defines relations as rotations from head entities to tail entities. By using the non-commutative composition property of rotations in the three-dimensional space, Rotate3D can naturally preserve the order of the composition of relations. Experiments show that Rotate3D outperforms existing state-of-the-art models for link prediction and path query answering. Further case studies demonstrate that Rotate3D can effectively capture various relation patterns with a marked improvement in modeling the composition pattern.

关键词： knowledge graph embedding Link Prediction Path Query Answering Relation Patterns

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Enhancing knowledge graph embedding with Relational Constraints 11

Enhancing Knowledge Graph Embedding with Relational Constrai...

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11th IEEE International Conference on knowledge graph (IEEE ICKG)

作者： Li, Mingda Sun, Zhengya Zhang, Siheng Zhang, Wensheng Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728181561

knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translation-based models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translation-based embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which possesses certain robust guarantee. We evaluate our method on the task of knowledge graph completion. Extensive experiments show that KRC achieves substantial improvements against baselines.

关键词： knowledge graph embedding relational constraints knowledge graph completion

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DualDE: Dually Distilling knowledge graph embedding for Faster and Cheaper Reasoning 22

DualDE: Dually Distilling Knowledge Graph Embedding for Fast...

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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Zhu, Yushan Zhang, Wen Chen, Mingyang Chen, Hui Cheng, Xu Zhang, Wei Chen, Huajun Zhejiang Univ Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Peking Univ Beijing Peoples R China Zhejiang Univ Hangzhou Innovat Ctr Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450391320

knowledge graph embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. However, high-dimensional KGEs pose huge challenges to storage and computing resources and are not suitable for resource-limited or timeconstrained applications, for which faster and cheaper reasoning is necessary. To address this problem, we propose DualDE, a knowledge distillation method to build low-dimensional student KGE from pre-trained high-dimensional teacher KGE. DualDE considers the dual-influence between the teacher and the student. In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher. Our DualDE is general enough to be applied to various KGEs. Experimental results show that our method can successfully reduce the embedding parameters of a high-dimensional KGE by 7x-15x and increase the inference speed by 2x-6x while retaining a high performance. We also experimentally prove the effectiveness of our soft label evaluation mechanism and two-stage distillation approach via ablation study.

关键词： knowledge graph embedding fast embedding knowledge distillation

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Locally Adaptive Translation for knowledge graph embedding 30

Locally Adaptive Translation for Knowledge Graph Embedding

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30th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence

作者： Jia, Yantao Wang, Yuanzhuo Lin, Hailun Jin, Xiaolong Cheng, Xueqi Chinese Acad Sci CAS Key Lab Network Data Sci & Technol Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9781577357605

knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

关键词： knowledge graph embedding Locally adaptive translation Optimal margin

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Efficient Non-Sampling knowledge graph embedding 21

Efficient Non-Sampling Knowledge Graph Embedding

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30th World Wide Web Conference (WWW)

作者： Li, Zelong Ji, Jianchao Fu, Zuohui Ge, Yingqiang Xu, Shuyuan Chen, Chong Zhang, Yongfeng Rutgers State Univ New Brunswick NJ 08901 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450383127

knowledge graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negative sampling, i.e., the model aims to maximize some similarity of the connected entities in the KG, while minimizing the similarity of the sampled disconnected entities. Negative sampling helps to reduce the time complexity of model learning by only considering a subset of negative instances, which may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such deficiency, we propose a new framework for KG embedding-Efficient Non-Sampling knowledge graph embedding (NS-KGE). The basic idea is to consider all of the negative instances in the KG for model learning, and thus to avoid negative sampling. The framework can be applied to square-loss based knowledge graph embedding models or models whose loss can be converted to a square loss. A natural side-effect of this non-sampling strategy is the increased computational complexity of model learning. To solve the problem, we leverage mathematical derivations to reduce the complexity of non-sampling loss function, which eventually provides us both better efficiency and better accuracy in KG embedding compared with existing models. Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.

关键词： knowledge graph embedding Non-Sampling Machine Learning Computational Efficiency Space Efficiency

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SpherE: Expressive and Interpretable knowledge graph embedding for Set Retrieval 47

SpherE: Expressive and Interpretable Knowledge Graph Embeddi...

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47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Li, Zihao Ao, Yuyi He, Jingrui Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798400704314

knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding of KGs, most of the current KG representation learning methods, where each entity is embedded as a vector in the Euclidean space and each relation is embedded as a transformation, follow an entity ranking protocol. On one hand, such an embedding design cannot capture many-to-many relations. On the other hand, in many retrieval cases, the users wish to get an exact set of answers without any ranking, especially when the results are expected to be precise, e.g., which genes cause an illness. Such scenarios are commonly referred to as "set retrieval". This work presents a pioneering study on the KG set retrieval problem. We show that the set retrieval highly depends on expressive modeling of many-to-many relations, and propose a new KG embedding model SpherE to address this problem. SpherE is based on rotational embedding methods, but each entity is embedded as a sphere instead of a vector. While inheriting the high interpretability of rotational-based models, our SpherE can more expressively model one-to-many, many-to-one, and many-to-many relations. Through extensive experiments, we show that our SpherE can well address the set retrieval problem while still having a good predictive ability to infer missing facts. The code is available at https://***/Violet24K/SpherE.

关键词： knowledge graph embedding Representation Learning

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GCE: Global Contextual Information for knowledge graph embedding 43rd

GCE: Global Contextual Information for Knowledge Graph Embed...

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43rd European Conference on IR Research

作者： Wang, Chen Zhong, Jiang Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China

ISBN: (纸本)9783030721121;9783030721138

Most existing large-scale knowledge graphs are suffering from incompleteness, and many research efforts have been devoted to the task of knowledge graph completion. One popular approach is to learn low-dimensional representations for all entities and relations, and then employ them to infer new facts. However, we find that most of the current knowledge graph embedding models are lack of suitable strategy to utilize global contextual information. In this paper, we propose an embedding model, named GCE, to explore the capability of global contextual information to the task of knowledge graph completion. In GCE, we carefully design a global contextual information module with the attention mechanism. This module could aggregate global contextual information adaptively, thus enhancing feature representation for knowledge graph completion. To demonstrate the effectiveness of our proposed GCE, we conduct extensive experiments on two benchmark datasets FB15k-237 and WN18RR. Experimental results show that GCE achieves competitive results compared with the existing state-of-the-art embedding models on both datasets. The results validate our central hypothesis - that global contextual information is beneficial to knowledge graph completion performance.

关键词： Global contextual information knowledge graph embedding knowledge graph completion Link prediction Attention mechanism

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cuKE: An Efficient Code Generator for Score Function Computation in knowledge graph embedding 38

cuKE: An Efficient Code Generator for Score Function Computa...

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International Parallel and Distributed Processing Symposium (IPDPS)

作者： Hu, Lihan Li, Jing Jiang, Peng Univ Iowa Iowa City IA 52242 USA Nvidia Santa Clara CA USA

ISBN: (纸本)9798350387117;9798350387124

knowledge graph embedding (KGE) plays an important role in graph mining and learning applications by converting discrete graph structures to continuous vector representations. While previous systems have focused on scaling KGE onto multiple GPUs, the score function computation on each GPU can be a performance bottleneck. Existing KGE systems implement the score functions with separate tensor operations, leading to large memory consumption and poor memory access efficiency. To overcome the issues, we propose a code generator that automatically translates Python-like definitions of KGE score functions into efficient CUDA code. Our code generator exploits the unique feature of KGE score functions and performs an aggressive fusion of tensor operations. Additionally, our generated code performs a runtime inspection to reduce redundant memory access for edges with identical indices. Experiments show that our generated code uses much less memory than previous systems and achieves an average speedup of 14.9x over TorchScript and 7.8x over TVM.

关键词： knowledge graph embedding code generation GPU

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On Integrating knowledge graph embedding into SPARQL Query Processing 25

On Integrating Knowledge Graph Embedding into SPARQL Query P...

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25th IEEE International Conference on Web Services (IEEE ICWS) Part of the IEEE World Congress on Services

作者： Kang, Hyunjoong Hong, Sanghyun Lee, Kookjin Park, Noseong Kwon, Soonhyun Elect & Telecommun Res Inst Daejeon South Korea Univ Maryland College Pk MD 20742 USA Univ N Carolina Charlotte NC 28223 USA

ISBN: (纸本)9781538672471

SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

关键词： SPARQL Query Processing knowledge graph embedding Nearest Neighbor Searching

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