CCF 学科前沿讲习班第 108 期：知识图谱融合方法，2019/11/9，北京
( CCF ADL #108: Tutorial on Knowledge Graph Fusion, 2019/11/9, Beijing )
知识图谱以结构化的方式描述客观世界中的概念、实体及其间的关系，将万维网的信息表达成更接近人类认知世界的形式，提供了一种更好地组织、管理和理解万维网上海量信息的能力。知识图谱可以由任何机构和个人自由构建，其背后的数据来源广泛、质量参差不齐，导致它们之间存在多样性和异构性。例如，对于相似领域，通常会存在多个不同的概念或实体指称真实世界中的相同事物。本报告首先简要介绍了语义网、知识图谱及知识图谱融合问题，然后介绍了面向知识图谱模式层的本体匹配方法，接下来介绍了面向知识图谱实例层的实体对齐方法，特别涉及近期基于表示学习的实体对齐方法，还介绍了知识融合过程中的真值推断方法，最后做了总结和展望。
胡伟，博士，南京大学计算机科学与技术系副教授、博士生导师。
如果您使用了本讲稿，请按如下引用：
( If you use the slides, please kindly cite it as follows: )
胡伟. 知识图谱融合方法. CCF 学科前沿讲习班, 2019
( Wei Hu. Tutorial on Knowledge Graph Fusion. The CCF Advanced Disciplines Lectures, 2019 )
本报告参考了以下论文和讲稿。由于报告人水平所限，错误和遗漏在所难免，恳切希望听众批评指正！
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