Kaisheng Zeng, Hailong Jin, Xin Lv, Fangwei Zhu, Lei Hou, Yi Zhang, Fan Pang, Yu Qi, Dingxiao Liu, Juanzi Li, Ling Feng
{"title":"XLORE 3:来自异构维基知识资源的大规模多语言知识图谱","authors":"Kaisheng Zeng, Hailong Jin, Xin Lv, Fangwei Zhu, Lei Hou, Yi Zhang, Fan Pang, Yu Qi, Dingxiao Liu, Juanzi Li, Ling Feng","doi":"10.1145/3660521","DOIUrl":null,"url":null,"abstract":"\n In recent years, Knowledge Graph (KG) has attracted significant attention from academia and industry, resulting in the development of numerous technologies for KG construction, completion, and application. XLORE is one of the largest multilingual KGs built from Baidu Baike and Wikipedia via a series of knowledge modelling and acquisition methods. In this paper, we utilize systematic methods to improve XLORE’s data quality and present its latest version, XLORE 3, which enables the effective integration and management of heterogeneous knowledge from diverse resources. Compared with previous versions, XLORE 3 has three major advantages: 1) We design a comprehensive and reasonable schema, namely XLORE ontology, which can effectively organize and manage entities from various resources. 2) We merge equivalent entities in different languages to facilitate knowledge sharing. We provide a large-scale entity linking system to establish the associations between unstructured text and structured KG. 3) We design a multi-strategy knowledge completion framework, which leverages pre-trained language models and vast amounts of unstructured text to discover missing and new facts. The resulting KG contains 446 concepts, 2,608 properties, 66 million entities, and more than 2 billion facts. It is available and downloadable online\n \n 1\n \n , providing a valuable resource for researchers and practitioners in various fields.\n","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"59 7","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"XLORE 3: A Large-scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge Resources\",\"authors\":\"Kaisheng Zeng, Hailong Jin, Xin Lv, Fangwei Zhu, Lei Hou, Yi Zhang, Fan Pang, Yu Qi, Dingxiao Liu, Juanzi Li, Ling Feng\",\"doi\":\"10.1145/3660521\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In recent years, Knowledge Graph (KG) has attracted significant attention from academia and industry, resulting in the development of numerous technologies for KG construction, completion, and application. XLORE is one of the largest multilingual KGs built from Baidu Baike and Wikipedia via a series of knowledge modelling and acquisition methods. In this paper, we utilize systematic methods to improve XLORE’s data quality and present its latest version, XLORE 3, which enables the effective integration and management of heterogeneous knowledge from diverse resources. Compared with previous versions, XLORE 3 has three major advantages: 1) We design a comprehensive and reasonable schema, namely XLORE ontology, which can effectively organize and manage entities from various resources. 2) We merge equivalent entities in different languages to facilitate knowledge sharing. We provide a large-scale entity linking system to establish the associations between unstructured text and structured KG. 3) We design a multi-strategy knowledge completion framework, which leverages pre-trained language models and vast amounts of unstructured text to discover missing and new facts. The resulting KG contains 446 concepts, 2,608 properties, 66 million entities, and more than 2 billion facts. It is available and downloadable online\\n \\n 1\\n \\n , providing a valuable resource for researchers and practitioners in various fields.\\n\",\"PeriodicalId\":5,\"journal\":{\"name\":\"ACS Applied Materials & Interfaces\",\"volume\":\"59 7\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Materials & Interfaces\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3660521\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3660521","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
XLORE 3: A Large-scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge Resources
In recent years, Knowledge Graph (KG) has attracted significant attention from academia and industry, resulting in the development of numerous technologies for KG construction, completion, and application. XLORE is one of the largest multilingual KGs built from Baidu Baike and Wikipedia via a series of knowledge modelling and acquisition methods. In this paper, we utilize systematic methods to improve XLORE’s data quality and present its latest version, XLORE 3, which enables the effective integration and management of heterogeneous knowledge from diverse resources. Compared with previous versions, XLORE 3 has three major advantages: 1) We design a comprehensive and reasonable schema, namely XLORE ontology, which can effectively organize and manage entities from various resources. 2) We merge equivalent entities in different languages to facilitate knowledge sharing. We provide a large-scale entity linking system to establish the associations between unstructured text and structured KG. 3) We design a multi-strategy knowledge completion framework, which leverages pre-trained language models and vast amounts of unstructured text to discover missing and new facts. The resulting KG contains 446 concepts, 2,608 properties, 66 million entities, and more than 2 billion facts. It is available and downloadable online
1
, providing a valuable resource for researchers and practitioners in various fields.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.