中国人工智能发展报告——知识工程（2019—2020）
| 作者 |
| 李德毅 等 |
| 丛书名 |
| 出版社 |
| 电子工业出版社 |
| ISBN |
| 9787121396564 |
| 简要 |
| 简介 |
| 内容简介 本书全面、客观地综述在云计算、大数据环境下知识工程领域面临的新的科学问题及技术挑战，展示国内外在知识工程领域所取得的最新进展，特别是对国内学者系统性、开创性的工作予以足够的重视并详细论述。本书针对大数据知识获取、表示、发现、开发与服务问题，围绕七大主题对国内外进展进行综述，包括大数据知识工程引论、知识表示、知识发现、知识管理与搜索、知识的智能建模、知识迁移和转换、知识工程交叉领域，这是在中国人工智能学会领导下，由知识工程与分布专委会组织编写的有关知识工程的具有重要学术价值和技术前瞻性的技术发展报告，适合人工智能及相关专业本科生、研究生、高级专业技术人员、政府科技管理人员阅读。 |
| 目录 |
| 第一部分 大数据知识工程引论 第1 章 传统知识工程到大数据知识工程 ····················································.2 1.1 人工智能与知识工程 ······································································.3 1.2 知识工程的发展 ·············································································.4 1.2.1 实验性系统阶段（1965～1974 年）··············································.4 1.2.2 MYCIN 阶段（1975～1980 年） ··················································.5 1.2.3 知识工程的应用阶段（1980 年至今） ··········································.5 1.3 大数据知识工程概念与处理框架 ···················································.5 1.3.1 HACE 定理 ···············································································.6 1.3.2 大数据知识工程模型—BigKE ···················································.8 1.4 大数据知识工程的应用场景 ·························································.10 1.4.1 大数据知识工程在电子商务领域的应用 ·······································.10 1.4.2 大数据知识工程在教育领域的应用 ·············································.10 1.4.3 大数据知识工程在医学领域的应用 ·············································.11 1.4.4 大数据知识工程在决策领域的应用 ·············································.12 1.4.5 大数据知识工程在华谱系统的应用 ·············································.12 1.5 小结：大人工智能时代 ·································································.13 参考资料 ································································································.13 第2 章 大知识与大知识工程 ·····································································.16 2.1 从大数据到大知识 ········································································.17 2.2 大知识与大知识系统 ·····································································.18 2.3 大知识工程 ···················································································.23 2.4 大知识工程实践和原型系统 ·························································.25 ·XIV· 2.5 小结 ·······························································································.26 参考资料 ································································································.27 第二部分 知 识 表 示 第3 章 开放知识图谱 ················································································.33 3.1 知识图谱简介 ················································································.33 3.2 知识图谱的价值 ············································································.35 3.2.1 辅助搜索 ·················································································.35 3.2.2 辅助问答 ·················································································.36 3.2.3 辅助大数据分析 ·······································································.37 3.2.4 辅助语言理解 ··········································································.37 3.2.5 辅助设备互联 ··········································································.38 3.3 开放的知识图谱项目 ·····································································.40 3.3.1 早期的知识库项目 ····································································.40 3.3.2 互联网时代的知识图谱 ·····························································.41 3.4 中文领域开放知识图谱：OpenKG ················································.44 3.4.1 中文领域知识图谱的开放现状 ····················································.44 3.4.2 开放的中文领域知识图谱Schema ···············································.45 3.4.3 中文开放知识图谱众包平台 ·······················································.46 3.5 多源信息融合与开放网络知识计算 ··············································.46 3.5.1 开放知识网络构建 ····································································.47 3.5.2 开放知识融合 ··········································································.48 3.5.3 开放知识网络特征 ····································································.49 3.6 大规模复杂网络的多元结构知识发现 ···········································.53 3.6.1 SBM 研究进展 ·········································································.56 3.6.2 小结 ·······················································································.58 参考资料 ································································································.59 第4 章 非规范知识表示与处理 ·································································.62 ·XV· 4.1 非规范知识处理的基本理论概述 ··················································.64 4.2 非规范知识处理研究成果概述 ······················································.66 4.3 基于模糊性的知识表示与学习 ······················································.71 4.3.1 知识表示参数的神经网络净化 ····················································.71 4.3.2 模糊极值熵理论 ·······································································.71 4.3.3 无监督学习的0.5-偏离模型和相应的计算策略 ······························.72 4.4 基于模糊粗糙集的建模与学习 ······················································.73 4.4.1 知识约减 ·················································································.73 4.4.2 模糊集合上下近似算子的构造 ····················································.73 4.4.3 基于模糊粗糙集的增量属性约简 ·················································.74 4.4.4 核函数框架下的模糊粗糙集 ·······················································.74 4.5 粗糙近似算子的构建与多粒度多标记模型 ···································.74 4.5.1 多粒度标记粗糙集知识表示模型 ·················································.74 4.5.2 粗糙集理论和证据理论 ·····························································.75 4.5.3 粗糙近似算子公理化 ·································································.75 4.5.4 多粒度模式知识发现的数据建模方法 ··········································.76 4.6 复杂环境下信息系统知识不确定度量 ···········································.76 4.6.1 不协调序信息系统的最大分布约简 ·············································.76 4.6.2 不确定性知识度量 ····································································.77 4.7 不完备与不一致信息的概念学习 ··················································.78 4.7.1 不完备概念知识表示 ·································································.78 4.7.2 不一致信息的概念知识表示 ·······················································.78 4.7.3 概念认知学习 ··········································································.78 参考资料 ································································································.79 第5 章 因素空间与知识表示的数学理论 ···················································.82 5.1 发展智能科学需要智能数学 ·························································.82 5.2 因素空间的内容、意义与方法 ······················································.85 ·XVI· 5.3 因素空间对知识工程的基本构想 ··················································.94 参考资料 ································································································.98 第6 章 知识粒计算—理论、模型与方法 ·············································.100 6.1 数据粒化 ·····················································································.101 6.2 粒计算模型 ·················································································.101 6.2.1 模糊集模型 ············································································.101 6.2.2 粗糙集模型 ············································································.102 6.2.3 商空间模型 ············································································.102 6.2.4 云模型 ··················································································.102 6.2.5 三支决策模型 ········································································.103 6.3 不确定性度量 ··············································································.103 6.4 粒计算推理 ·················································································.103 6.5 多粒度模型与方法 ······································································.104 6.5.1 多粒度计算模型 ·····································································.104 6.5.2 多粒度不确定性度量 ·······························································.104 6.5.3 多粒度模式发现与融合 ···························································.105 6.6 粒计算的应用研究 ······································································.105 参考资料 ······························································································.106 第7 章 时空知识表示与推理 ···································································.108 7.1 定性空间关系模型 ······································································.109 7.2 定性空间关系推理 ······································································.109 7.3 时空知识推理应用 ······································································.110 参考资料 ······························································································.111 第三部分 知 识 发 现 第8 章 大数据知识发现—挑战与应对 ·················································.115 8.1 大数据知识发现技术挑战 ···························································.115 ·XVII· 8.1.1 描述与存储的挑战 ··································································.115 8.1.2 分析与理解的挑战 ··································································.116 8.1.3 挖掘与预测的挑战 ··································································.116 8.2 大数据知识发现技术研究成果 ····················································.116 8.2.1 大数据处理技术 ·····································································.116 8.2.2 大数据挖掘与知识发现 ···························································.119 8.3 大数据实践 ·················································································.121 8.4 小结 ·····························································································.122 参考资料 ······························································································.123 第9 章 大数据知识发现—理论与技术 ·················································.125 9.1 描述性统计方法 ··········································································.125 9.2 可视化数据挖掘方法 ···································································.126 9.3 机器学习方法 ··············································································.127 9.4 国内研究现状 ··············································································.129 参考资料 ······························································································.130 第10 章 富格式文本中的知识发现 ··························································.135 10.1 背景 ···························································································.135 10.2 文档结构识别 ············································································.140 10.3 自然语言语义提取 ····································································.142 10.4 表格语义提取 ············································································.146 参考资料 ······························································································.147 第四部分 知识管理与搜索 第11 章 大数据挖掘与知识管理 ······························································.154 11.1 研究背景 ···················································································.154 11.2 智能知识管理研究概述 ·····························································.156 11.3 智能知识管理基本概念 ·····························································.157 ·XVIII· 11.4 智能知识管理研究现状 ·····························································.160 11.5 未来研究方向 ············································································.162 参考资料 ······························································································.163 第12 章 智能知识管理·············································································.164 12.1 引言 ···························································································.164 12.2 智能知识管理研究概况 ·····························································.166 12.3 智能知识的挖掘算法与技术 ······················································.167 12.3.1 转化规则挖掘的方法 ·····························································.167 12.3.2 基于多目标线性规划的二次挖掘方法及其在信用卡客户流失 管理中的应用 ·······································································.168 12.3.3 智能知识管理系统设计技术 ···················································.168 12.4 知识可拓优化技术 ····································································.169 12.4.1 研究概况 ·············································································.169 12.4.2 知识可拓自分类优化技术 ·······················································.169 12.4.3 知识可拓自聚类优化技术 ·······················································.171 12.4.4 知识可拓自识别优化技术 ·······················································.171 12.5 小结 ···························································································.172 参考资料 ······························································································.175 第13 章 基于认知的多媒体大数据驱动知识搜索 ····································.178 13.1 大数据驱动的知识搜索 ·····························································.178 13.2 大数据异质媒体搜索环境下的认知行为规律分析 ····················.180 13.2.1 传统桌面搜索场景下的用户认知行为 ······································.181 13.2.2 泛在设备搜索场景下的用户认知行为 ······································.182 13.3 面向大数据泛在搜索环境的异质媒体搜索 ·······························.183 13.3.1 面向异质媒体资源的表示学习方法 ··········································.183 13.3.2 图像资源表示学习 ································································.184 13.3.3 音频资源表示学习 ································································.184 ·XIX· 13.3.4 视频资源表示学习 ································································.184 13.3.5 面向带噪声数据的学习排序方法 ·············································.185 13.3.6 数据驱动的带噪声数据学习排序 ·············································.185 13.3.7 算法驱动的带噪声数据学习排序 ·············································.185 13.4 面向复杂查询的异质媒体搜索技术 ··········································.186 13.4.1 复杂查询的搜索意图理解与推理 ·············································.186 13.4.2 基于知识图谱的搜索意图理解 ················································.186 13.4.3 基于智能问答的搜索意图理解 ················································.187 13.4.4 异质媒体数据聚合与深度排序 ················································.188 13.4.5 泛在搜索场景用户交互形式分析建模 ······································.188 第五部分 知识的智能建模 第14 章 智能体系统 ················································································.192 14.1 概述 ···························································································.192 14.2 智能体ABGP 模型 ····································································.194 14.2.1 环境感知 ·············································································.194 14.2.2 信念 ····················································································.196 14.2.3 目标 ····················································································.196 14.2.4 规划 ····················································································.197 14.3 智能体系统结构 ········································································.197 14.4 脑机融合推理与决策 ·································································.198 14.4.1 脑机融合的自动推理 ·····························································.200 14.4.2 脑机融合的协同决策 ·····························································.200 参考资料 ······························································································.201 第15 章 多智能体协同与智能博弈 ··························································.203 15.1 概述 ···························································································.203 15.2 多智能体学习 ············································································.207 ·XX· 15.3 多智能体协作 ············································································.208 15.4 分布式规划 ················································································.209 15.5 算法博弈论 ················································································.210 15.6 安全博弈论 ················································································.211 15.7 智能对弈 ···················································································.212 15.8 未来展望 ···················································································.213 参考资料 ······························································································.213 第16 章 脑机融合的信息处理 ·································································.217 16.1 引言 ···························································································.217 16.2 脑机融合的混合智能 ·································································.218 16.3 混合智能的信息回路模型 ·························································.220 16.4 脑机融合的信息处理框架 ·························································.222 16.5 脑机融合信息处理的实现思路初探 ··········································.224 16.6 小结 ···························································································.226 参考资料 ······························································································.227 第六部分 知识迁移和转换 第17 章 迁移学习 ····················································································.229 17.1 迁移学习算法 ············································································.230 17.1.1 基于样本实例重新加权的迁移学习算法 ···································.230 17.1.2 基于模型融合集成的迁移学习算法 ··········································.230 17.1.3 基于特征选择的迁移算法 ·······················································.231 17.1.4 基于特征映射的迁移算法 ·······················································.231 17.1.5 基于深度学习的迁移学习算法 ················································.232 17.2 迁移学习研究前景及挑战 ·························································.233 参考资料 ······························································································.235 第18 章 可拓知识工程与知识转换 ··························································.240 ·XXI· 18.1 可拓信息—知识—策略形式化体系 ··········································.241 18.2 基于可拓规则的知识表示方法 ··················································.242 18.3 可拓知识获取 ············································································.244 18.3.1 可拓分类知识获取 ································································.245 18.3.2 传导知识获取 ·······································································.246 18.3.3 基于知识库的可拓知识获取 ···················································.247 18.4 不相容问题求解 ········································································.247 18.4.1 可拓策略生成方法 ································································.248 18.4.2 可拓策略生成系统 ································································.250 18.5 小结 ···························································································.251 参考资料 ······························································································.251 第七部分 知识工程交叉领域 第19 章 基于知识的软件工程 ·································································.255 19.1 全过程基于知识方法 ·································································.255 19.2 基于环境建模的需求获取和分析 ··············································.258 19.3 基于深度学习的软件代码分析和生成 ·······································.261 19.4 知件和基于知件的软件工程 ······················································.262 参考资料 ······························································································.264 第20 章 农业大数据知识服务 ·································································.266 20.1 农业大数据的知识获取 ·····························································.267 20.1.1 基于智能引导的人工知识获取 ················································.267 20.1.2 自动和半自动知识获取 ··························································.268 20.2 农业知识库构建 ········································································.269 20.2.1 知识表示策略 ·······································································.269 20.2.2 推理机制 ·············································································.270 20.2.3 专家系统开发平台 ································································.270 ·XXII· 20.2.4 知识发现系统开发平台 ··························································.271 20.3 农业知识服务 ············································································.271 参考资料 ······························································································.275 第21 章 深度学习与自然语言处理 ··························································.278 21.1 自然语言处理 ············································································.278 21.2 深度学习 ···················································································.279 21.2.1 深度学习的提出与发展 ··························································.279 21.2.2 深度学习的基本原理 ·····························································.280 21.3 深度学习在自然语言处理中面临的问题 ···································.281 21.4 深度学习在自然语言处理中的应用 ··········································.282 21.5 未来深度学习在自然语言处理中的发展 ···································.284 21.6 小结 ···························································································.284 参考资料 ······························································································.285 第22 章 互联网谣言检测与知识辨伪 ······················································.287 22.1 技术背景 ···················································································.288 22.2 谣言检测相关定义 ····································································.290 22.3 谣言检测任务的难点 ·································································.291 22.4 谣言检测最新技术 ····································································.292 22.4.1 基于深度学习的谣言文本模式挖掘 ··········································.292 22.4.2 基于深度学习的谣言视觉模式挖掘 ··········································.293 22.5 小结 ···························································································.294 参考资料 ······························································································.294 第八部分 展 望 第23 章 知识工程领域未来展望 ······························································.299 23.1 知识表示与推理 ········································································.300 23.2 知识驱动的智能技术 ·································································.302 ·XXIII· 23.3 机器学习可解释性研究 ·····························································.303 23.4 深度学习与传统方法的结合 ······················································.305 23.5 小样本学习研究 ········································································.307 23.6 小结 ···························································································.309 参考资料 ······························································································.309 后记 ············································································································.313 外国人名中英文对照表 ·······································································.315 外国机构名中英文对照表 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