Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Amazon（亚马逊）；SUSTech（华南理工大学）；Zhejiang University（浙江大学）；Yale University（耶鲁大学）；Carnegie Mellon University（卡内基梅隆大学）；HKUST (GZ)（香港科技大学）；Nanyang Technological University（南洋理工大学）；Microsoft（微软）
Uncovering Intrinsic Capabilities: A Paradigm for Data Curation in Vision-Language Models
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Hong Kong Baptist University, China（香港 Baptist大学）；City University of Hong Kong, China（香港城市大学）
Data to Defense: The Role of Curation in Customizing LLMs Against Jailbreaking Attacks
Stony Brook University（石溪大学）；Binghamton University（宾汉姆顿大学）；University of Iowa（爱荷华大学）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）
Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
University of Cambridge（剑桥大学）；OATML, University of Oxford（牛津大学OATML实验室）；University of Toronto（多伦多大学）；Shanghai Jiao Tong University（上海交通大学）；Samsung AI Center（三星人工智能中心）
CurateGPT: A flexible language-model assisted biocuration tool
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Queen Mary University of London（伦敦玛丽女王大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所（欧洲分子生物学实验室-欧洲生物信息研究所））；Berlin Institute of Health - Charité, Universitätsmedizin（柏林健康研究所-夏里特医科大学）
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Centre for Big Data Research in Health, University of New South Wales（健康大数据研究中心，新南威尔士大学）；Eastern Heart Clinic, Prince of Wales Hospital（东部心脏诊所，王室医院）；NSW Ambulance Aeromedical Operations, Bankstown Helicopter Base（新南威尔士州急救航空医疗运作，班克stown直升机基地）；Department of Anaesthesia, Saint George Hospital（麻醉科，圣乔治医院）；Department of Cardiology, Prince of Wales Hospital（心内科，王室医院）；School of Clinical Medicine, University of New South Wales（临床医学学院，新南威尔士大学）
ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）
