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Top1. Introduction
The hierarchy of human knowledge is categorized at the levels of data, information, knowledge, and intelligence. Knowledge bases and databases are significantly different in both theories and manipulation mechanisms in cognitive informatics and cognitive computing (Hayes-Roth et al., 1983; Debenham, 1989; Krishna, 1992; Miller, 1995; Bender, 1996; Ullman & Widom, 1997; Fellbaum, 1998; Pojman, 2003; Brewster et al., 2004; Leone et al., 2006; Wang, 2008a, 2009b, 2010b, 2012a, 2013b, 2014g, 2015; Hu et al., 2010). According to basic studies in cognitive science and neurophysiology (Hampton, 1997; Pojman, 2003; Leone et al., 2006; Wang, 2003, 2012b, 2013a, 2014c; Wang & Wang, 2006; Wang & Fariello, 2012), the foundations of human knowledge in long-term memory can be represented by an object-attribute-relation model (Wang, 2007a) based on the synaptic structure of human memory, which represents the hierarchical and dynamic neural clusters of knowledge retained in memory as well as the logical model of knowledge bases.
Conventional technologies for knowledge base modeling and manipulations can be classified into three categories known as linguistic knowledge bases (Crystal, 1987; Debenham, 1989; Miller, 1995; Pullman, 1997; Fellbaum, 1998; Liddy, 2001; Wang & Berwick, 2012, 2014), expert knowledge bases (Hayes-Roth et al., 1983; Bender, 1996; Wang, 2009c, 2012c), and ontology (Smith & Medin, 1981; Gruber, 1993; Miller, 1995; Cocchiarella, 1996; Brewster et al., 2004; Tiberino et al., 2005; Leone et al., 2006; Sanchez, 2010; Wang, 2008a, 2015a; Wang et al., 2011). Typical linguistic knowledge bases are lexical databases such as WordNet and ConceptNet (Miller, 1995; Fellbaum, 1998; Berners-Lee, 2001; Liu & Singh, 2004). Expert knowledge bases are represented by various logical rule-based systems (Hayes-Roth et al., 1983; Bender, 1996) and fuzzy rule-based systems (Zadeh, 1965, 2004; Surmann, 2000; Wang, 2014d, 2014e, 2015c). Ontology treats a small-scale knowledge as a set of words and their relations in a certain domain (Gruber, 1993; Tiberino et al., 2005; Leone et al., 2006; Sanchez, 2010; Wang et al., 2011). It is recognized that main problems of conventional methodologies for knowledge bases are man-made rather than machine built, the lack of rigorous and adequate operations on acquired knowledge, inflexible for learnt knowledge synergy, and the weak transformability among different knowledge bases (Wang, 2015a; Wang et al., 2011).