{"title":"利用图形处理器快速完成知识图谱","authors":"Chun-Hee Lee , Dong-oh Kang , Hwa Jeon Song","doi":"10.1016/j.jpdc.2024.104885","DOIUrl":null,"url":null,"abstract":"<div><p>Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate <span><math><mi>N</mi><mo>×</mo><mi>N</mi><mo>×</mo><mi>R</mi></math></span> vector operations, where <em>N</em> is the number of entities and <em>R</em> is the number of relation types. It is very costly.</p><p>In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define <em>transformable to a metric space</em> and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is <em>transformable to a metric space</em>. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.</p></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast knowledge graph completion using graphics processing units\",\"authors\":\"Chun-Hee Lee , Dong-oh Kang , Hwa Jeon Song\",\"doi\":\"10.1016/j.jpdc.2024.104885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate <span><math><mi>N</mi><mo>×</mo><mi>N</mi><mo>×</mo><mi>R</mi></math></span> vector operations, where <em>N</em> is the number of entities and <em>R</em> is the number of relation types. It is very costly.</p><p>In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define <em>transformable to a metric space</em> and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is <em>transformable to a metric space</em>. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.</p></div>\",\"PeriodicalId\":54775,\"journal\":{\"name\":\"Journal of Parallel and Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Parallel and Distributed Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0743731524000492\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Parallel and Distributed Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0743731524000492","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
摘要
知识图谱可用于许多与数据语义相关的领域,如问题解答系统、基于知识的系统等。然而,目前构建的知识图谱需要进行补充,以获得更好的知识关系。这就是所谓的知识图谱补全。要使用知识图谱嵌入模型为现有知识图谱添加新的关系,我们必须评估 N×N×R 向量运算,其中 N 是实体的数量,R 是关系类型的数量。在本文中,我们在 GPU 上提供了一个高效的知识图完成框架,利用知识图嵌入向量获取新关系。在所提出的框架中,我们首先定义了可转换为度量空间的模型,然后提供了一种将知识图完成问题转换为可转换为度量空间的模型的相似性连接问题的方法。之后,为了有效地处理相似性连接问题,我们利用度量空间的特性推导出公式。基于这些公式,我们开发了一种快速知识图完成算法。最后,我们通过实验证明,我们的框架可以高效地处理知识图完成问题。
Fast knowledge graph completion using graphics processing units
Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate vector operations, where N is the number of entities and R is the number of relation types. It is very costly.
In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.
期刊介绍:
This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing.
The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.