A Formal Knowledge Retrieval System for Cognitive Computers and Cognitive Robotics

A Formal Knowledge Retrieval System for Cognitive Computers and Cognitive Robotics

Yingxu Wang, Yousheng Tian
DOI: 10.4018/ijssci.2013040103
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Intelligent knowledge base theories and technologies are fundamentally centric in machine learning and cognitive robotics. This paper presents the design of a formal knowledge retrieval system (FKTS) for intelligent knowledge base modeling and manipulations based on concept algebra. In order to rigorously design and implement FKTS, real-time process algebra (RTPA) is adopted to formally describe the architectures and behaviors of FKTS. The architectural model of FKTS in the form of a set of unified structure models (USMs) is rigorously described. On the basis of USMs, functional models of FKTS are hierarchically refined by a set of unified process models (UPMs). The UPMs of FFTS are divided into two subsystems known as those of the knowledge visualization and knowledge base retrieval subsystems where the content-addressed searching mechanism is implemented in knowledge bases manipulations. The FKTS system is design and implemented as a part of the cognitive learning engine (CLE) for cognitive computers and cognitive robots.
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1. Introduction

Intelligent knowledge bases theories and supporting technologies for knowledge manipulations are recognized as central problems in machine learning, cognitive computing, and cognitive systems. There is an increasing demand on advanced theories and technologies for content-addressed knowledge retrieval from intelligent knowledge bases (Wilson & Keil,2001; Chang, 2006; Reisinger & Pasca, 2009; Wang, 2009b; Wang et al., 2011, 2012b).

Conventional technologies for knowledge base modeling and manipulations are linguistic knowledge bases (Crystal, 1987; Debenham, 1989; Pullman, 1997; Fellbaum, 1998; Liddy, 2001; Wang et al., 2012b), expert knowledge bases (Bender, 1996; Wilson & Keil,2001; Wang, 2012l), and ontology (Brewster et al., 2004; Leone et al., 2006; Yan, 2006; Poesio & Almuhareb, 2008; Wang, 2009b; Wang et al., 2011). Typical linguistic knowledge bases are lexical databases such as WordNet and ConceptNet (Fellbaum, 1998; Liu & Singh, 2004; Wang & Berwick, 2012). Expert knowledge bases are represented by various logical rule-based systems (Bender, 1996) and fuzzy rule-based systems (Zadeh, 1965, 2004; Surmann, 2000; Wang, 2012j). Ontology is a branch of metaphysics dealing with the nature of being, which treats a small-scale knowledge as a set of words and their semantic relations in a certain domain (Gruber, 1993; Cocchiarella, 1996; Brewster et al., 2004; Tiberino et al., 2005; Sanchez, 2010; Hao, 2010; Wang et al., 2011). However, problems of linguistic knowledge bases are that they only provide materials or resources for applied knowledge bases. Human knowledge modeling and retrieval is more complicated beyond logical rules. Further, ontology may only represents a set of static knowledge and is highly subjective and application-specific, which prevents machines from automatically modeling and manipulating different levels of knowledge in knowledge bases.

In recent studies in cognitive informatics (Wang, 2002a, 2003, 2006a, 2006b, 2007c, 2007e, 2009a, 2009e, 2009f, 2009h, 2009i, 2009k, 2010a-b, 2011d, 2012d-g, 2012i, 2012k, 2012m, 2013a-e; Wang & Wang, 2006; Wang & Fariello, 2012l; Wang et al., 2003, 2006, 2009a-b, 2010a-b, 2012a, 2013) and cognitive computing (Wang, 2007b, 2008f, 2009e, 2009f, 2010a, 2010b, 2010d, 2011b; Wang et al., 2010c), it is recognized that concepts are the basic unit of human thinking, reasoning, and communications (Pojman, 2003; Wang, 2008b). A generic internal knowledge representation theory known as the Object-Attribute-Relation (OAR) model is developed by Wang (2007d) based on neurophysiological evidences (Wilson & Keil, 2001; Wang & Fariello, 2012). The OAR model provides a logical view of the long-term memory of the brain, which shows that human memory and knowledge are represented by relations via synaptic connections between neurons rather than by the neurons themselves as the traditional container metaphor suggested.

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