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Evolution of Knowledge Representation and Retrieval Techniques I.J. Intelligent Systems and Applications, 2015, 07, 18-28 Published Online June 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2015.07.03 Evolution of Knowledge Representation and Retrieval Techniques Meenakshi Malhotra Dayanand Sagar Institutions, RIIC, Bangalore, 560078, India Email: uppal_meenakshi @yahoo.co.in T. R. Gopalakrishnan Nair Saudi Aramco Endowed Chair, Technology and Information Management, PMU KSA Email: [email protected], [email protected] Abstract— Existing knowledge systems incorporate knowledge knowledge as structured information whereas wisdom is retrieval techniques that represent knowledge as rules, facts or a knowledge in use [7,38]. The hierarchical model depicts hierarchical classification of objects. Knowledge representation four components of the pyramid that are linked linearly, techniques govern validity and precision of knowledge retrieved. along with interconnectivity among the four components There is a vital need to bring intelligence as part of knowledge as shown fig. 1. Volume of content involved reduces retrieval techniques to improve existing knowledge systems. Researchers have been putting tremendous efforts to develop towards the vertex of the DIKW pyramid, shown in fig.1. knowledge-based system that can support functionalities of the However the usability of the content provided at each human brain. The intention of this paper is to provide a level, increases towards the vertex [57]. Increased reference for further research into the field of knowledge wisdom implies more relational connections within the representation to provide improved techniques for knowledge content resulting in an increase of available useful retrieval. This review paper attempts to provide a broad information. overview of early knowledge representation and retrieval Data that forms the basis of human information system techniques along with discussion on prime challenges and issues has no meaningful existence of its own without its ability faced by those systems. Also, state-of-the-art technique is to inter-connect. Strength of connectivity between data discussed to gather advantages and the constraints leading to further research work. Finally, an emerging knowledge system points distinguishes data from information. Connected that deals with constraints of existing knowledge systems and information, when used to perform a task or provide a incorporates intelligence at nodes, as well as links, is proposed. solution to a given problem, is treated as knowledge. Knowledge in turn coupled with experiences embodies Index Terms— Informledge System, Knowledge-Based wisdom to the system. Systems, Knowledge Graphs, Ontology, Semantic Web In addition to four components of DIKW, intelligence and innovation also belong to the pyramid. Knowledge is referred as intelligence when applied to derive solutions I. INTRODUCTION to problems in an efficient way. Intelligence, when Information sharing has been one of the important applied to a new task, is said to be innovation and lies aspects of human interactions. From cave paintings to the between knowledge and wisdom in the DIKW pyramid. It current World Wide Web (WWW) the need to share is this intelligence, which needs to be incorporated into information has led to technological changes. WWW has the knowledge and information retrieval system in hand. emerged as a huge information storage that accumulate immense information from numerous domains. Initially only data was stored and used in its raw form, subsequently it was structured to provide data as useful information. This information has strewn on the web for a long time leading to the quest for knowledge to be retrieved through defined reasoning from the stored information [29]. Studies in the field of knowledge have put forward a differentiation among data, information, knowledge and wisdom as data-information-knowledge-wisdom (DIKW) hierarchy [55]. DIKW hierarchy is also referred to as knowledge hierarchy or information hierarchy or more commonly as knowledge pyramid [3, 15]. The knowledge pyramid represents data in its raw form Fig. 1. DIKW Pyramid. that can further exist in any form and can be recorded. Section II provides an overview over related work Information is referred as relationally connected data and done for the preliminary knowledge systems and section Copyright © 2015 MECS I.J. Intelligent Systems and Applications, 2015, 07, 18-28 Evolution of Knowledge Representation and Retrieval Techniques 19 III discusses some of the knowledge representation and retrieval schemes used later. Section IV discusses state- of-the-art knowledge system and its techniques. Section V briefs about the future scope and upcoming intelligent knowledge systems and finally section VI provides the conclusion. II. RELATED WORK The need to utilize data effectively and retrieve substantial results has been the focus since computer Fig. 2. Classification of Explicit Knowledge. systems were invented. Knowledge representation has arisen as a major discipline of Artificial Intelligence (AI) Knowledge representation involves different schemes in computer science. There is no universally accepted namely logical schemes, procedural schemes, networked definition of AI. AI is a combination of other fields schemes, structured schemes [56]. Networked scheme has namely machine learning, knowledge representation, seen continuous growth over time. On the other hand, ontology-based search, Natural Language Processing logical and procedural scheme works with a fixed set of (NLP), neural networks, image processing, pattern symbols and instructions that get limited with the recognition, robotics, expert systems, and many others increase in information to be encoded. Structured [52]. As defined by Barr & Feigenbaum, “Artificial schemes utilize a complex structure for node in the graph Intelligence (AI) is part of computer science concerned thereby restricting its wide usage [46]. Networked with designing intelligent computer systems, that is, schemes have simple nodes in the graph, which stores systems that exhibit characteristics we associate with data and allows an enormous amount of data to be intelligence in human behavior – understanding language, embedded into the system in the form of nodes. learning, reasoning, solving problems, and so on” [4]. Tolman had introduced the concept of cognitive maps Knowledge representation has been the main [66]. Cognitive maps provide mental representation of component involved in constructing intelligent spatial information as knowledge [30]. However, knowledge systems and knowledge-based systems. cognitive map does not possess any of the cognitive Knowledge has been the main focal point for knowledge processing of its own. Kosko introduced a fusion of fuzzy representation [13, 29, 56]. Knowledge, as possessed by logic and the cognitive map as Fuzzy Cognitive Maps human brain, has been classified broadly into two type’s (FCM) [2]. FCM utilizes fuzzy logic to compute the namely tacit and explicit knowledge [4]. Tacit knowledge, strength of the relations. In 1976, Sowa developed also known as informal knowledge, is defined as conceptual graphs (CG) to represent the logic based on knowledge that is hard to share as the same cannot be put the semantic network using a graph [60, 61]. A CG is a across completely through vocabulary. It is gained finite, connected bipartite graph where a node either through experiences, intuition, insights and observations. represents concept or conceptual relationship. Arcs are It is said to be within the subconscious human mind. only allowed between concept and the conceptual Contrary to tacit knowledge is explicit knowledge that is relationship and not between two concepts or two easy to share, communicate and store by means of a conceptual relationships. The Conceptual Graph combination of different vocabularies. It is also referred Interchange Format (CGIF) is a dialect specified to as articulated knowledge [52, 63]. The existing express common logic provided in CG [62]. However, information and knowledge system deals with explicit CG was merely a structured representation of given knowledge that is further categorized as shown in fig. 2. information that was difficult to scale up and also lacked Domain knowledge: It represents knowledge pertaining intelligence. to a specific group. Concept Maps (CM), designed and established by Declarative knowledge: It describes what is known Novak and Gowin [48], is a hierarchical structure that about the problem. depicts hierarchy of concepts through the relationship Procedural Knowledge: This knowledge provides between concepts. Here the concepts are represented by direction on how to do a particular task or provide a words that are linked through labeled arc. CM is used to solution. understand the relationship between words as concepts Commonsense knowledge: General purpose knowledge [47]. CM has found its usability in learning as well as in supposed to be present with every human being. assessing learning for a small number of connected Heuristic Knowledge: Describes a rule-of-thumb that concepts. It is also used to depict structuring of guides the reasoning process. Heuristic knowledge is organizations and help administrators to manage often called shallow knowledge. organizations [12]. However, CM does not provide any Meta Knowledge: Describes knowledge about structure for knowledge retrieval. Some of the important knowledge. Experts use this knowledge to enhance the issues faced while dealing with the above mentioned
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