引用本文:李青,钟将,李立力,等.领域专业知识富关联关系提取方法[J].控制与决策,2021,36(1):52-60
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领域专业知识富关联关系提取方法
李青1, 钟将1, 李立力2, 张剑3, 李琪4
(1. 重庆大学计算机学院,重庆400044;2. 重庆大学土木工程学院,重庆400044;3. 重庆西信天元数据资讯有限公司,重庆401121;4. 绍兴文理学院计算机科学与工程系,浙江绍兴312000)
摘要:
面向知识服务业中领域专业内容资源的多模态、智能化、精细化、知识化和重组化的碎片性管理需求,如何高效生成和应用专业知识,促进实体经济创新发展,成为共同的战略选择与难题.对此,重点研究八大战略新兴产业内容资源的富关联体系和知识关系标引规范,制定面向服务专业内容资源的一致性富关联关系的描述体系.构建内容资源表示实体(知识、信息、资源、服务、对象)间的富关联模式,满足实体间自动解构、聚合及智能抽取的需求,提出基于领域专业知识的富关联关系提取模型.运用多层注意力机制来凸显重要表征性信息,通过知识图谱设计并优化异构环境中核心源对象与目标对象间元属性.与以往基线模型不同,所提出的模型结构支持在特定领域下端到端的学习,不必显式依赖外部知识.实验结果表明,领域专业知识富关联关系提取方法,可有效提升富关联关系识别精度及专业知识服务效率.
关键词:  富关联关系  领域专业知识  关系提取  自然语言处理  人工智能  深度学习
DOI:10.13195/j.kzyjc.2019.0727
分类号:TP273
基金项目:国家重点研发计划项目(2017YFB1402400);中央高校研究生科研创新项目(2018CDYJSY0055);重庆市研究生科研创新项目(CYB18058);重庆市技术创新与应用示范项目(cstc2018jszx-cyzdX0086);重庆市重点产业共性关键技术创新专项(cstc2017zdcy-zdyf0150);陕西省教育厅科学技术研究项目(18JK1130).
Extraction method of multiple semantic relations in domain knowledge
LI Qing1,ZHONG Jiang1,LI Li-li2,ZHANG Jian3,LI Qi4
(1.College of Computer Science,Chongqing University,Chongqing400044,China;2.School of Civil Engineering,Chongqing University,Chongqing400044,China;3.Chongqing Xixintianyuan Data Information Co.,Ltd., Chongqing401121,China;4.Department of Computer Science and Engineering,Shaoxing University,Shaoxing312000,China)
Abstract:
For the fragmentary management needs of multi-modal, intelligent, refined, knowledgeable and reorganized professional content resources in the knowledge service industry, how to efficiently generate and apply professional knowledge to promote the innovation and development of the real economy has become a common strategic choice and a challenging problem. Therefore, this paper studies the multi-relation classification system and knowledge relationship labeling standard of content resources in eight strategic emerging industries, and develops a consistent multi-relation classification description system for service-oriented professional content resources. Then an extraction model of multiple semantic relations based on domain knowledge is proposed, which can distinguish various entities (e.g., knowledge, information, resources, services, objects), and meet the requirements of automatic deframe, aggregation and intelligent extraction among entities. A multi-level attention mechanism is used to highlight representational details. At the same time, it designs and optimizes meta-attributes between the core source and the target in heterogeneous contexts through the knowledge graph. Unlike previous baseline models, the proposed model structure supports end-to-end learning in the specific domain without explicit dependence on external knowledge. The experiments show that the proposed method can effectively improve the accuracy of the multi-relation classification and the efficiency of professional knowledge service.
Key words:  multiple semantic relation  domain knowledge  relation extraction  natural language processing  artificial intelligence  deep learning

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