|
|
|
| Research on the Methods of Information Science and Artificial Intelligence Fusion Innovation |
| Wen Youkui1,2, Wen Hao3, and Qiao Xiaodong1,2 |
1.Institute of Science and Technology Information of China, Beijing 100038
2.Beijing Wanfang Data Co., Ltd., Beijing 100038
3.School of Information and Control Engineering, Xi an University of Architecture Technology, Xi an 710055 |
|
|
|
|
Abstract The improvement of computer hardware performance and the development of cloud computing technology have increased the speed of scientific literature information retrieval and multi-type data clustering problems. However, the objects of retrieval cannot directly enter the factual knowledge of a document s content; thus, it is difficult to realize intelligent technology. Literature Big Data Knowledge quickly answers questions and recommends service functions. The browsing of scientific and technological literature information in the context of big data continually increases the time spent by scientific and technological personnel to obtain innovative knowledge and the burden that is placed on them. There are two reasons for this. One is that the data model of scientific literature is an unstructured text data structure, and the other is that the database of traditional information retrieval systems does not support unstructured text data structures. These two points have constrained the development of scientific and technological literature on big data results and led to user issues with artificial intelligence and automated answering services. In response to this problem, this paper proposes intelligent mining and knowledge service research based on the achievements of big data innovation in scientific literature. Firstly, it utilizes the idea of ??artificial intelligence to uncover innovative results in scientific and technological literature, then it establishes a semantic knowledge base for innovative results, and it finally establishes a semantic knowledge base intelligent inference engine problem-answer service system. This study explores the research methods for the intelligent and automated development of the browsing model of a big data service for scientific literature.
|
|
Received: 04 July 2018
|
|
|
|
1 赖茂生. 科技文献检索[M]. 北京: 北京大学出版社, 1985.
2 科技文献检索的途径、方法、技术和步骤是什么[EB/OL].[2018-05-10].https://zhidao.baidu.com/question/544759988.html.
3 情报检索计算机化[EB/OL]. [2018-05-10]. http://www.danwiki.com/hddawiki/index.php?doc-view-2319.
4 张琪玉. 情报检索语言基础[M]. 武汉: 武汉大学出版社, 1997.
5 温浩, 温有奎. 主题成因的知识元本体转换模型研究[J]. 情报学报, 2011, 30(11): 1123-1128.
6 CalvaneseD, GiacomoG D, LemboD, et al. Tractable reasoning and efficient query answering in description logics: The DL-lite family[J]. Journal of Automated Reasoning, 2007, 39(3): 385-429.
7 StonebrakerM. SQL databases v. NoSQL databases[J]. Communications of the ACM, 2010, 53(4): 10.
8 GajendranS K. A survey on NoSQL databases[J]. University of Illinois, 2012.
9 ZomayaA Y, SakrS. Handbook of big data technologies[M]. Cham: Springer, 2017.
10 FeldmanR, SangerJ. The text mining handbook: Advanced approaches in analyzing unstructured data[M]. Cambridge University Press, 2006.
11 温有奎. 基于语义挖掘的文本知识元模型与应用研究[D]. 武汉: 武汉大学, 2010.
12 KellyJ E, HammS. Smart machines: IBM’s Watson and the era of cognitive computing[M]. Columbia University Press, 2013.
13 YooI. Semantic text mining and its application in biomedical domain[D]. Philadelphia: Drexel University, 2006.
14 HuangZ H, ChenH Z, YuT, et al. Semantic text mining with linked data[C]// Proceedings of the Fifth International Joint Conference on INC, IMS and IDC. 2009: 338-343. |
|
|
|
