Research on Knowledge Fusion Connotation and Process Model
The emergence of big-data brings diversified structures and constant growths of knowledge. The objective of knowledge fusion (KF) research is to integrate, discover and exploit valuable knowledge from distributed, heterogeneous and autonomous knowledge so
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School of Information Management, Wuhan University, Wuhan 430072, Hubei, People’s Republic of China [email protected] 2 Department of Computer Science, University of Wisconsin-La Crosse, La Crosse, WI 54601, USA
Abstract. The emergence of big-data brings diversified structures and constant growths of knowledge. The objective of knowledge fusion (KF) research is to integrate, discover and exploit valuable knowledge from distributed, heterogeneous and autonomous knowledge sources, which is the necessary prerequisite and effective approach to implement knowledge services. In order to apply KF practice, this paper firstly discusses KF connotations in terms of analysing the relations and differences among various notions, i.e. knowledge fusion, knowledge integration, information fusion and data fusion. Then, based on the knowledge representation method using ontology, this paper investigates several KF implementation patterns and provides two types of dimensional KF process models oriented to demands of knowledge services.
Keywords: Knowledge fusion pattern · Process mode
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Knowledge representation
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Fusion
Introduction
With the development of data creating, releasing, storing and processing technologies, data is showing a rapid growth trend in all society areas. Of all the data available to the human civilization, 90% were produced in the past two years, the big data era has arrived [16]. Knowledge is awareness and understanding about people or things in the objective world, which is generated by feeling, communicating and logic inference activities in the course of practice and education and maybe facts, information or skills. The information chain, formed with “fact → data → information → knowledge → wisdom”, indicates that big data contains huge amount of information, from which large knowledge can be extracted. Big data gives rise to the emergence of large scale knowledge bases. Famous knowledge base research projects, e.g. DBpedia, KnowItAll, NELL and YAGO, use information extraction techniques acquiring knowledge from high quality network data sources (e.g. Wikipedia), and automatically realize its construction and management [22]. Meanwhile, big data brings about information overload c Springer Nature Singapore Pte Ltd. 2016 H. Chen et al. (Eds.): CCKS 2016, CCIS 650, pp. 184–195, 2016. DOI: 10.1007/978-981-10-3168-7 18
Research on Knowledge Fusion Connotation and Process Model
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and pollution too, in which knowledge presents characteristics of heterogeneity, diversity and independence. In the era of data, with rapidly increasing of information and knowledge, knowledge discovery has become the research focus in various disciplines, including data science and information science [25]. Therefore, in order to improve the efficiency and quality of knowledge service, issues of analysing and utilizing knowledge existing in big data, eliminating the inconsistency between different knowledge sources, and extracting, discovering and inducing the potential valuable connotations, have become important in knowl
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