基于知识图谱的包装产业信息查询技术架构
Packaging Industry Information Inquiry Technology Architecture Based on Knowledge Graph
DOI: 10.12677/CSA.2017.79098, PDF, HTML, XML, 下载: 1,707  浏览: 2,740  科研立项经费支持
作者: 朱文球, 司 元:湖南工业大学计算机学院,湖南 株洲
关键词: 包装产业信息知识图谱本体知识库信息查询Packaging Industry Information Knowledge Graph Ontology Knowledge Base Information Inquiry
摘要: 提出了基于知识图谱的包装产业信息查询技术架构,并对知识体系构建、知识抽取、知识融合和知识应用等核心技术进行了阐述。提出一种基于领域本体的问题分类方法和结构化语义信息提取方法,根据给定的种子模板,从大规模训练数据中可以自动学习相关的模板。以中国包装产业数据库搜索为例,提出一种能处理自然语言查询的基于知识图谱的中国包装产业数据库查询方法,给出了基于知识图谱的中国包装产业数据库查询系统的具体构建步骤。
Abstract: The packaging industry information inquiry technology architecture based on knowledge graph is proposed, and the knowledge hierarchy construction, the knowledge extraction, the knowledge fusion, and the knowledge application are described. In this paper, we introduce a new method of classifying questions with the help of domain-specific ontology and obtain structural semantic information for the question. Given a seed pattern, relevant pattern can be learned automatically from large-scale training corpus. The packaging industry database search method based on the knowledge graph for handling query natural language query is proposed, and the constructing procedures of the packaging industry information inquiry system based on knowledge graph are provided.
文章引用:朱文球, 司元. 基于知识图谱的包装产业信息查询技术架构[J]. 计算机科学与应用, 2017, 7(9): 858-868. https://doi.org/10.12677/CSA.2017.79098

参考文献

[1] 蒋锴, 钱夔, 郑玄, 等. 基于知识图谱的军事信息搜索技术架构[J]. 指挥信息系统与技术, 2016, 7(1): 47-52.
[2] Haveliwala, T.H. (2003) Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. IEEE Transactions on Knowledge & Data Engineering, 15, 784-796.
https://doi.org/10.1109/TKDE.2003.1208999
[3] 贾真, 杨宇飞, 何大可, 等. 面向中文络百科的属性和属性值抽取[J]. 北京大学学报: 自然科学版, 2014, 50(1): 41-47.
[4] 怀宝兴, 宝腾飞, 祝恒书, 等. 一种基于概率主题模型的命名实体链接方法[J]. 软件学报, 2014, 25(9): 2076-2087.
[5] 金贵阳, 吕福在, 项占琴. 基于知识图谱和语义网技术的企业信息集成方法[J]. 东南大学学报(自然科学版), 2014, 44(2): 250-255.
[6] Xin, R.S., Gonzalez, J.E., Franklin, M.J., et al. (2013) GraphX: A Resilient Distributed Graph System on Spark. International Workshop on Graph Data Management Experiences and Systems, New York, 22-27 June 2013, 1-6.
[7] 陆晓华, 张宇, 钱进. 基于图数据库的电影知识图谱应用研究[J]. 现代计算机, 2016(7): 76-83.
[8] 袁旭萍. 基于深度学习的商业领域知识图谱构建[D]: [硕士学位论文]. 上海: 华东师范大学, 2015.
[9] 王仁武, 袁毅, 袁旭萍. 基于深度学习与图数据库构建中文商业知识图谱的探索研究[J]. 图书与情报, 2016(1): 110-117.
[10] Zheng, W.G., Zou, L., et al. (2015) How to Build Templates for RDF Question/Answering: An Uncertain Graph Similarity Join Approach. SIGMOD Conference, Melbourne, 31 May-4 June, 2015, 1809-1824.