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ISSN 2249-6343 International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 2 A Tour Towards Knowledge Representation Techniques Tilotma Sharma1, Deepali Kelkar2 b) The knowledge base K is being queried about a fact f. Outcome depends upon KR paradigm used, may be yes, no, unknown, yes with a confidence factor of A ...etc. 3. KR as the embodiment of AI systems: There are identical interconnected units that are collectively responsible for representing various concepts. A concept is represented in a Distributed sense and is indicated by an evolving pattern of activity over a collection of units. In conventional computing the data is stored in data base whereas in AI the knowledge base is used to store the knowledge required for solving the particular task. Abstract- Knowledge Representation (KR) is the area of AI concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. KR is a combination of data structure and interpretive procedures that leads to knowledgeable behavior. In this paper, an attempt is made to shed more light on KR, and to look at some techniques for it in the real world, with the view to understand their relevance and to realize their crucial usefulness in the development of effective knowledge based (KB) systems. This paper explains various declarative knowledge representations techniques. II. KNOWLEDGE B ASE SYSTEM MODEL/ ARCHITECTURE : The KR system must be able to represent any type of knowledge, “Syntactic, Semantic, logical, Presupposition, Understanding ill formed input, Ellipsis, Case Constraints, Vagueness”[3]. For making it more effective the knowledge representation model is divided in to five different parts the K Box, Knowledge Base, Query applier, reasoning and user interface as shown in fig 2.1. K Box :- The first part of K Box takes The input from the outside world through user interface. The source of input can be a book, novel, News paper etc. The Input from the user is divided into two categories either it can be a new information or it can be the query. If incoming input is the new information then it goes to the Acquisition and learning process to check whether that knowledge is already in knowledge base if yes then system will discard that. Otherwise it checks whether that knowledge will be accommodate by the existing system if yes then segmentation process has been done on the input to check in which categories it lies and separates the action with the other. Feature Extraction part of K Box is used to check whether there is an activity can be perform or some process is to be present in the incoming text for Ex. Mobile is ringing then the process is going on in this incoming knowledge means some sound is coming and the root of ringing is ring. If the sentence is like ram is a boy then no action will be performed. If the incoming knowledge is simple sentence then we can represent it by using semantic net, frames and predicate logic but when some activity could be performed by the entity then we need a structure that could be dynamic in nature and must be expressive. Keywords: Knowledge Representation, Predicate Logic, Semantic Nets, Frames, Scripts. I. INTRODUCTION An Expert (Knowledge Based) System is a problem solving and decision making system based on knowledge of its task and logical rules or procedures for using knowledge. Both the knowledge and the logic is obtained from the experience of a specialist in the area (Business Expert). An Expert System is a program that emulates the interaction a user might have with a human expert to solve a problem. The end user provides input by selecting one or more answers from a list or by entering data. The program will ask questions until it has reached a conclusion[1]. A knowledge representation (KR) is an idea to enable an individual to determine[2] consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. There are two basic components of knowledge representation i.e. reasoning and inference. In fact KR is the fundamental issue in AI that attempt to understand intelligence. There are three wide perspectives of knowledge representation . 1. KR as applied epistemology: All intelligent system presupposes knowledge which is represented in a knowledge base that consists of knowledge structures (normally symbolic) and programs. 2. KR as a tell-ask module: KR system should provide at least two operations: a) For a given knowledge base K, with the facts f. It must be resulting in a new knowledge base, K'. 131 ISSN 2249-6343 International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 2 Knowledge Structure part of K Box is used to represent the incoming knowledge by using best knowledge representation technique. The KR is combination of Semantic Net and Script techniques. Knowledge Base consist all the knowledge required to solve the problem. The knowledge base can be general or domain specific. Query Applier is used for getting the facts from the system and then passes the data to the inference mechanism for reasoning. Whenever the new query comes system will learn whether that query is related to the previous query or it generates from the previous query and check how many time user ask the combination of these. Reasoning is used for getting new fact from the existing knowledge. The simplest reasoning technique is forward and backward reasoning. Declarative knowledge refers to representation of objects and events, knowledge about facts and relationships. It is the knowledge about “that something is true or false”, for example, a car has 4 tyres, Peter is older than Robert. Concepts, objects, facts, propositions, assertions, semantic nets, logic and descriptive models. All declarative knowledge are explicit knowledge[5]. Fig 3.1: Relationship among types of knowledge III. KNOWLEDGE REPRESENTATION T ECHNIQUES : 3.1) Knowledge Representation Using Predicate Logic: Predicate logic is a formal language (like programming language) with rules for syntax (i.e. how to write expressions) and semantics (i.e. how to formalize the meaning of expressions)[6]. Syntax are well formed formulas that includes logical symbols, predicate and function symbols, term, formula and sentence. Semantics means meaning of a term or formula i.e. set of elements. The meaning of a sentence is a truth value. The function that maps a formula into a set of elements is called an interpretation. An interpretation maps an intensional description (formula/sentence) into an extensional description (set of truth value). First-order logic extends propositional logic in two directions first it provides an inner [2]structure for sentences. They are viewed as expressing relations between objects or individuals. Second It provides a means to express, and reason with, generalizations. In predicates logic there are three additional notations. 1)Terms: in First-order logic are used to represent objects or individuals. Terms can be a constant designate specific object) For e.g. A, B, Smith, Blue, etc, variable (designate unspecified object): x, y, z, etc, and Functions (designate a specific object related in a certain way to another object, or, objects):Father Of, Color Of. Fig2.1: Knowledge Base System Model/Architecture Types Of Knowledge: Knowledge is categorized into 2 major types i.e. Tacit and Explicit. The term “Tacit” corresponds to informal or implicit type of knowledge. The term “Explicit” corresponds to formal type of knowledge. Tacit knowledge is drawn from experience, action, subjective insight whereas Explicit knowledge is drawn from artifact of some type as principle, procedure, process, concepts. These artifacts of explicit knowledge are used in the knowledge creation process to create 2 types of knowledge i.e. Declarative and Procedural Knowledge[4]. Procedural Knowledge focuses on tasks that must be performed to reach a particular objective or goal. It is knowledge about “how to do something”, for example, to determine if Peter or Robert is older, first we have to find their ages. Procedures, rules, strategies, agendas, models belongs to procedural knowledge. They are tacit knowledge. 132 ISSN 2249-6343 International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 2 2) Predicates: Predicates is defined as a relation that binds two atoms have a value of true or false. A predicate can take arguments, which are terms. A predicate with one argument expresses a property of an object for e.g. Student(Bob).A predicate with two or more arguments expresses a relation between objects for e.g .likes(Bob, Mary). Predicate with no arguments is just a simple proposition logic. 3) Universal Quantifier: are used to identify the scope of the variable in a logical expression. For e.g. x P(x) means “for all x, P of x is true”. Example: x Happy (x) If the universe of discourse is people, then this means that everyone is happy. Other examples: x y Knows(x, y) => Knows(y, x), x y Knows(x, y) ^ Knows(y, x), x y Knows(x, y) => ¬ Likes(y, x). 4) Existential Quantifier: if the statement is x P(x) means “there exists at least one x for which P of x is true”. Example: x Happy(x),If the universe of discourse is people, then this means there is at least one happy person. Other examples: x y Knows(x,y), x y Knows(x, y) ^ Knows(y, x) . x y Knows(x, y) => ¬ Likes(y, x). iv) Semantic: It establishes the way of associating the meaning. Nodes and links denote application specific entities. Inheritance is one of the main kind of reasoning done in semantic nets. The ISA (is a ) relation is often used to link a class and its superclass [8]. Fig4.2.2: Hierarchical Semantic Network 4.3) Knowledge Representation Using Frames: A frame is a node with additional structure that facilitates differentiating relationships between objects and properties of objects. Sometimes it is called as “slot-and-filler” representation. Frame overcome the limitation of semantic network that differentiates relationships and properties of objects. Each frame represents a class (set) or an instance (an element of a class)[9]. Frames are application of objectoriented programming for expert systems. The concept of a frame is defined by collection of slots. Each slot describes a particular attribute or operation of the frame. Slots are used to store values. A slot may contain a default value or a pointer to another frame, a set of rules or procedure by which the slot value is obtained[10][11]. 4.2) Knowledge Representation Using Semantic Net: Semantic networks are an alternative to predicate logic as a form of knowledge representation. The knowledge can be store in the form of graph, with nodes representing objects in the world, and arcs representing relationships between those objects[7]. Semantic network also called as Associative Network. Fig4.2.1: Semantic Network Semantic representation consists of 4 parts: i) Lexical: It tells which symbols are allowed in the representation’s vocabulary. Nodes denote objects, links denote relation between objects, link-labels denote particular relations. ii) Structural: It describes constraints on how the symbols can be arranged. Nodes are connected to each other by links. iii) Procedural: It specifies the access procedures (to create, modify, answer questions). Procedures are constructor procedure, reader procedure, writer procedure and erasure procedure. Fig 4.3.1: frames 133 ISSN 2249-6343 International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 2 Table4.3.1: Tabular representation of the frame from one view. 10. 11. 12. objective or goal Knowledge about “how to do something” Examples: procedures, rules, strategies, agendas, models All procedural knowledge are tacit knowledge and relationships. Knowledge about “that something is true or false” Examples: concepts, objects, facts, propositions, assertions, semantic nets, logic and descriptive models All declarative knowledge are explicit knowledge V. CONCLUSION KR is the study of how what we know can at the same time be represented as comprehensibly as possible and reasoned with as effectively as possibly[12]. The simplest analysis shows difference between procedural and declarative knowledge. KR is very important for knowledge based systems. A selected KR scheme should have appropriate inference methods to allow for reasoning. Popular KR schemes are Rules, Semantic Nets, Schemata(Frames and Scripts) and Logic. Balance must be found between effective representations, efficiency and understandability for effectiveness. Effective KR should be used to represent the most important aspects of the real world, such as action, space, time, mental events. 4.4) Knowledge Representation Using Scripts: A script is a remembered precedent, consisting of tightly coupled, expecting-suggesting primitive action and statechange frames. A script is a structured representation describing a stereotyped sequence of events in a particular context[11]. Scripts predict unobserved events. Scripts can form a coherent account from disjoint conversations. As compared to scripts, a frame is a relatively large chunk of knowledge about a particular object, event, location, situation or other element. The frame describes the object in great detail. Script, on the other hand, is a knowledge representation scheme that instead of describing an object, describes a sequence of events. V I. REFERENCES: [1] “The Basics of Expert (Knowledge Based) Systems”, 1997 by JM & Co/AJRA. [2] Poonam Tanwar, Dr. T.V. Prasad, Dr. Mahendra S. Aswal, “ Comparative Study of Three Declarative Knowledge Representation Techniques”, International Journal on Computer Science and Engineering, Vol. 02, No. 07, 2010, 2274-2281. [3] Poonam Tanwar, Dr. T.V. Prasad, Dr. Kamlesh Dutta, “An Effective Knowledge Base System Architecture and Issues in Representation Techniques”, International Journal on Computer Science and Engineering, Vol. 02, No. 07, 2010, 2274-2281. [4] RC Chakraborty, “Artificial Intelligence Knowledge Representation Issues, Predicate Logics, Rules”, http://myreaders.wordpress.com/, Feb 2, 2008. [5] Elaine Rich and Kelvin Knight, Carnegie Mellon University, “Artificial Intelligence”, 2006. [6] Uta Priss “Predicate Logic” Set 07106 Mathematics for Software Engineering, School of Computing, Edingburg Napier University, 2010. [7] Matthew Huntbach, Dept. of Computer Science, Queen Mary and Westfield College, London, “Artificial Intelligence I”, 1996. IV. COMPARISON B ETWEEN P ROCEDURAL AND DECLARATIVE KNOWLEDGE : S.No. Procedural Declarative Knowledge Knowledge 1. Hard to debug Easy to validate 2. Black Box White Box 3. Obscure Explicit 4. Process Oriented Data Oriented 5. Extension may affect Extension is easy stability 6. Fast, direct execution Slow (requires interpretation) 7. Simple data can be May require high level used data type 8. Representation in the Representation in the form of sets of rules, form of production organized into system, the entire set of routines and rules for executing the subroutines task 9. Focuses on tasks that Refers to representations must be performed to of objects and events, reach a particular knowledge about facts 134 ISSN 2249-6343 International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue 2 [8] KR Chowdhary, Professor and Head, Dept. Of Computer Science and Engineering, MBM Engineering College, Jodhpur, “Artificial Intelligence (Semantic Networks)”, August 4, 2011. [9] “Knowledge Representation”, http://www.cs.cf.ac.uk/ Dave/AI2/node32.html, 28 August 2001. [10] Negnevitsky, “Frame-based Expert Systems”, Pearson Education, 2002. [11] M. Kerber, “Knowledge Representation I”, Introduction to AI 06-08775, 2004/05. [12] Usman Babawuro, Zou Beiji, School of Information Science and Engineering, Central South University Changsa, Hunan, PR China, “Knowledge Representation: A General Survey and Techniques for Sound Knowledge Based Systems”, International Journal of Intelligent Information Processing (IJIIP), Vol. 2, Number 4, December 2011. 135