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Top1. Introduction
The famous perception on knowledge is Francis Bacon’s assertion in 1597 that “Knowledge is power.” Knowledge is acquired and comprehended information generated by the brain, which is embodied as a concept and its relations to existing ones. Knowledge can be classified into two main categories as those of conceptual and behavior knowledge [Berkeley, 1710; Boole, 1854; Russell, 1956; Dancy, 1985; Wilson & Keil, 1999; Pojman, 2003; Wang, 2003; 2009c; 2012a; 2012b]. The former are created by knowing to-be such as abstract knowledge and experience, while the latter are acquired by knowing to-do such as behaviors and skills.
Knowledge science is an emerging field that studies the nature of human knowledge, principles and formal models of knowledge representation, and theories for knowledge manipulations such as creation, generation, acquisition, composition, memorization, retrieval, and depository in knowledge engineering. All scientific, engineering, and humanity disciplines generate and process knowledge. The following survey highlights related disciplines such as philosophy, cognitive science, linguistics, computer science, information science, neuroinformatics, cognitive informatics, and mathematics that contribute to the development of knowledge science.
Studies on knowledge in philosophy form the domain of epistemology [Berkeley, 1710; Boole, 1854; Russell, 1956; Dancy, 1985]. Studies on knowledge in cognitive science reveal the mechanisms of knowledge acquisition, storage, and retrieval in the brain [Matlin, 1998; Gabrieli, 1998; Wilson & Frank, 1999; Wang, 2002; 2009c; 2012c]. Studies on knowledge in linguistics result in syntactic and semantic theories [Chomsky, 1965; Keenan, 1975; Saeed, 2009; Zadeh, 1997; 2004; Wang, 2013b; Wang & Berwick, 2012; 2013], linguistic knowledge bases [Crystal, 1987; Miller, 1995], and cognitive linguistics [Pullman, 1997; Evans & Green, 2006; Wang & Berwick, 2012, 2013]. Studies on knowledge in computer and information sciences lead to the establishment of artificial intelligence [Shannon, 1948; Turing, 1950; McCarthy et al., 1955; von Neumann, 1958; Debenham, 1989; Albus, 1991; Gruber, 1993; Brewster et al., 2004; Wang, 2007a; 2010; 2012b; 2012c; 2014b; 2015a; 2015b], and machine learning [Gagne, 1985; Bender, 2000; Wang, 2013b; 2015f]. Studies on knowledge in neuroinformatics [Wilson & Keil, 1999; Hampton, 1997; Gabrieli, 1998; Wang, 2003, 2009b, 2012b, 2013a; Wang & Wang, 2006;] deepen the understanding of internal knowledge representation as the object-attribute-relation (OAR) model [Wang, 2007c] and the neural circuit theory for knowledge representation [Wang & Fariello, 2012]. Studies on knowledge in cognitive informatics [Wang, 2002, 2003, 2006, 2007b, 2008b, 2009a, 2009b, 2009c; Wang et al., 2006; 2009a; 2009b; 2010] lead to the layered reference model of the brain (LRMB) [Wang et al., 2006] that provides the context of knowledge and learning with the support of other cognitive processes. In order to rigorous explain the framework of knowledge manipulation, a theory of cognitive knowledge base (CKB) [Wang, 2014a] and a cognitive system known as the cognitive learning engine (CLE) [Wang, 2013b; Hu, Wang, & Tian, 2010] are developed. Studies in mathematics create a set of denotational mathematical structures [Wang; 2007a; 2007b; 2008a; 2009d; 2012e; 2015e; 2015f; 2016] such as concept algebra [Wang, 2008d; 2015f], semantic algebra [Wang, 2013b], inference algebra [Wang, 2011; 2012d] for rigorous manipulations of formal knowledge in knowledge science and engineering.