| A Study on Big Knowledge and Its Engineering Issues | |
Lu, Ruqian1,2; Jin, Xiaolong3,4; Zhang, Songmao1,2 ; Qiu, Meikang5; Wu, Xindong6,7
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| 2019-09-01 | |
| Source Publication | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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| ISSN | 1041-4347 |
| Volume | 31Issue:9Pages:1630-1644 |
| Abstract | After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. |
| Keyword | Big data knowledge engineering big data knowledge engineering big knowledge massiveness characteristics big-knowledge system big-knowledge engineering life cycle |
| DOI | 10.1109/TKDE.2018.2866863 |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2016YFB1000902] ; NSFC[61472412] ; NSFC[61572473] ; NSFC[61621003] ; NSFC[91746209] ; NSFC[61772501] ; US National Science Foundation (NSF)[1763620] ; US National Science Foundation (NSF)[1652107] ; Program for Yangtze River Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education (China)[IRT17R32] ; Beijing Science and Technology Project on Machine Learning based Stomatology ; Tsinghua-Tencent-AMSSCJoint Project on WWW Knowledge Structure and its Application |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000480352800001 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/35410 |
| Collection | 数学所 |
| Corresponding Author | Lu, Ruqian |
| Affiliation | 1.Chinese Acad Sci, Key Lab MADIS, AMSS, Inst Math, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Key Lab IIP, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China 4.Univ CAS, Sch Comp & Control Engn, Beijing 100190, Peoples R China 5.Columbia Univ, Dept Elect Engn, New York, NY 10027 USA 6.Hefei Univ Technol, Res Inst Big Knowledge, Hefei 230009, Anhui, Peoples R China 7.Univ Louisiana Lafayette, Sch Comp & Informat, Lafayette, LA 70504 USA |
| Recommended Citation GB/T 7714 | Lu, Ruqian,Jin, Xiaolong,Zhang, Songmao,et al. A Study on Big Knowledge and Its Engineering Issues[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2019,31(9):1630-1644. |
| APA | Lu, Ruqian,Jin, Xiaolong,Zhang, Songmao,Qiu, Meikang,&Wu, Xindong.(2019).A Study on Big Knowledge and Its Engineering Issues.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,31(9),1630-1644. |
| MLA | Lu, Ruqian,et al."A Study on Big Knowledge and Its Engineering Issues".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 31.9(2019):1630-1644. |
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