Knowledge Representation using Semantic Net and Fuzzy Logic

Knowledge Representation using Semantic Net and Fuzzy Logic Poonam Tanwar1, Mandeep Kaur2, Dr. T. V. Prasad3 1,2

Asst. Professor, 3 Professor & Head Dept. of CSE, Lingaya’s University, Faridabad, Haryana, India Email: [email protected], [email protected], [email protected],

Abstract – The knowledge representation is the fundamental issue in AI that attempt to understand intelligence. Basically knowledge representation is a study of methods of how knowledge is actually picturized and how effectively it resembles the representation of knowledge in human brain. It is noted that the fuzzy logic is also one of the most effective techniques in AI, which provides a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. The knowledge can be contained within the fuzzy-logic rule base which makes a very simple and competent concept to use for any real world application. Fuzzy logic relates to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. In this paper we introduced the basic concepts and techniques underlying the use of fuzzy logic to knowledge representation and also explained the way of representing the knowledge via Semantic Net.

1.

Keywords: knowledge representation, fuzzy logic, semantic network.

There are various kinds of knowledge that require to be represented in AI systems for eg. Objects, Events, Performance, meta-knowledge etc. The relation between facts and their representation is shown in Fig.1 [2].

2.

3.

1. INTRODUCTION A knowledge representation (KR) is an idea to enable an individual to determine 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. It is a way of efficient computation in which thinking is accomplished. Knowledge representation is an essential area for cognitive science and AI. In former it is concerned with how people store and process information and in later the main aim is to store knowledge so that programs can process it. Constructing an intelligent system, require large amount of knowledge and a method for representing large amounts of knowledge that permits their effective use and interaction. In fact knowledge representation is the fundamental issue in AI that attempt to understand intelligence. There are three wide perspectives of knowledge representation [18][3].

KR as applied epistemology: All intelligent system presupposes knowledge. Knowledge is represented in a knowledge base, which consists of knowledge structures (normally symbolic) and programs. KR as a tell-ask module: KR system should provide at least two operations:  For a Given knowledge base K, with the facts f. It must be resulting in a new knowledge base, K'.  The knowledge base K is being queried about a fact f. The outcome depends upon the KR paradigm used, may be yes, no, unknown, yes with a confidence factor of A ...etc. 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.

Fig.1 Relation between facts and their representation First, Objects are the Facts about objects in real world domain. e.g. Chair has four lags, it can be

1

wooden or plastic. Second, Events are actions that occur in real world. e.g. Ram is sitting on the chair. Third, Performance is a behavior like playing the violin involves knowledge about how to do things. Fourth, Meta-knowledge is the knowledge about what we know. e.g. Shawna is preparing for exams. It knows that it can read the syllabus for clearing the exam. Thus AI deals with Facts and rules. Knowledge representation is the major area of AI that provides the way of representing the facts and an inference system for manipulating the facts.

network, it represents the connection between objects or class of objects. It is a directed graph in which nodes / vertices are used to represent the objects/ class of objects and edges or link (unidirectional) is used to represent the semantic relations between the objects. Semantic network are generally used to represent the inheritable knowledge. Inheritance is most useful form of inference. Inheritance is the property in which element of some class inherit the attribute and values from some other class as shown in Fig.2. To support inheritance object must be organized into classes and classes must be arranged in a generalization hierarchy.

1.1 Knowledge representation techniques Currently there are many techniques for representing the knowledge such as List and tree(graph)Which is used to represent the hierarchical knowledge. Semantic networks in which nodes and links are used to store the propositions. Schemas which is used to represent commonsense knowledge.Frames and scripts are the commonly used Schemas.Frames Describe the objects which Consist of a sets of nodes and links Knowledge represented by frames is organised in slots. Frames are hierarchically organised. Scripts are used to describe the event rather than objects. Consist of stereotypically ordered causal or temporal chain of events. Rule-based knowledge representations basically used in problem-solving contexts. It Involves production rules containing if-then or situation-action pairs. Rule based or problem space representations Contain: Initial state.  Goal state.  Legal operators which are the things you are allowed to do.  Operator restrictions. Logic-based representations may use deductive or inductive reasoning that Contain: Facts and premises.  Rules of propositional logic and Rules of predicate calculus that allows use of additional information about objects in the proposition, use of variables and functions of variables.  Measures of certainty which involve Certainty Factors for eg. If symptom then (CF) diagnosis) [20].

Fig.2 Property of inheritance Sometimes Semantic nets are also called as associative nets because nodes are associated or related to others node as there is an activation spreading form one concept node to other nodes This types of relationships have proven particularly useful in a wide variety of knowledge representations. Commonly used links in semantic nets are i.e IS-A, and A-KIND-OF. IS-A means is an instance of or refers to a member of some class whereas A-KINDOF represents the link from one class to other class as shown in Fig 3 [17].

2. KNOWLEDGE REPRESENTATION USING SEMANTIC NET Fig.3 IS-A and KIND-OF link A semantic network is widely used knowledge representation technique. As the name semantic

2

All semantic networks are a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Following are six of the most common kinds of semantic networks. 1. Definitional networks 2. Assertional networks 3. Implicational networks 4. Executable networks 5. Learning networks 6. Hybrid networks

Since knowledge can be expressed in a more natural way by using fuzzy sets, many engineering and decision problems can be greatly simplified. In the fuzzy theory, fuzzy set A of universe X is defined by function µA(x) called the membership function of set A µA(x): X  {0, 1}, where

Here for any element x of universe X, membership function µA(x) equals the degree to which x is an element of set A. This degree, a value between 0 and 1, represents the degree of membership, also called membership value, of element x in set A. The knowledge using fuzzy logic is represented by the production system [5]. Production system consists of four components. 1. A set of rules/rule base each contains the left hand side that deterdmine the applicability of the rule and a right hand side that determined which operation to be performed if the rule is applied. 2. Knowledge base that contained whatever information is required to solve the problem. It may be permanent or may be used for the current problem. 3. A control strategy that specify the order in which the rules will be compared to the knowledge base and a way of resolving the conflicts that arise when several rules match at once. 4. Interpreter/rule applier solves the control problem, i.e., decide which rule to execute on each selection-execute cycle. Let us consider an example of production rule:  IF (at airport AND check ins) THEN action(get on the plane)  IF (on airport AND not paid AND have credit card) THEN action(pay with card) AND add(paid)  IF (on airport AND paid AND empty seat) THEN sit down

The various types of semantics net are shown in Table 1 in annexure 1 [1] [12] [14].

3. KNOWLEDGE REPRESENTATION USING FUZZY LOGIC In 1965 Lotfi Zadeh introduced the multi-valued logic i.e. Fuzzy Logic which extended the range of truth values to all real numbers in the interval between 0 and 1 whereas in case of crisp set the truth values are defined as either 0 or 1 as shown in Fig 4 [15]. For example, the possibility that the sun is shinny when there are some clouds in the sky might have a set to a value of 0.7. It is likely that the sun is shinning. Basically, fuzzy logic is the way to represent expert knowledge that uses vague and ambiguous terms. Fuzzy logic is a set of mathematical principles for knowledge representation based on the theory of fuzzy sets, sets that calibrate vagueness and degrees of membership. Generally, the membership funcitions are used to represent a fuzzy set are sigmoid, Gaussian and pi.  (x) X

Fuzzy Subset A 1

0 Crisp Subset A

Fuzziness

Fuzziness

µA(x) = 1 if x is totally in A; µA(x) = 0 if x is not in A; 0 < µA(x) < 1 if x is partly in A.

x

Here the conditions and actions must be clearly defined so that can easily be expressed in any programming language.

Fig. 4 Crisp and Fuzzy Set Fuzzy set theory resembles human reasoning in its use of approximate information and uncertainty to generate decisions. So to depict the knowledge in more precise way the fuzzy logic is the way which is designed to mathematically represent uncertainty and vagueness and provide formalized tools for dealing with the imprecision built-in to many problems.

4. CONCLUSION Knowledge representation provide the way to represent all the above defined things i.e. the collection of information used for solving the

3

[9] Zadeh, L.A. (1988), “Fuzzy Logic”, Proceeding of IEEE-CS Computer Society, vol.21, 4, 1988, pp.83-93. [10] ]Zadeh, L.A. (1989), “Knowledge Representation in Fuzzy Logic”, Proceeding of IEEE Transactions on Knowledge and Data Engineering, vol.1, 1, pp.89-100. [11] John Sowa on Knowledge Representation – http://www.jfsowa.com/ [12] http://www.wiziq.com/tutorial/485-Introductionto-Artificial-Intelligence [13] http://www.dcs.qmul.ac.uk/~mmh/AINotes/AIN otes4.pdf [14] http://www.science.uva.nl/research/sne/ndl/ [15] www.doc.ic.ac.uk/~sgc/teaching/v231/lecture4.p pt [16] automatika.etf.bg.ac.yu/files/predmeti/os4ns2/Fu zzy_logic_1.ppt [17] http://www.se.cuhk.edu.hk/~seg7450/lecture/Se mantic-Net-Frame.pdf [18] R. Davis, H. Shrobe, and P. Szolovits. What is a Knowledge Representation? AI Magazine, 14(1):17-33, 1993 [19] Brachman R, Levesque H, eds.,” Readings in Knowledge Representation”, Los Altos: Morgan Kaufman. 1985. [20] Stillings , Luger” Knowledge Representation “Chapters 4 and 5); (1994)). http://www.acm.org/crossroads/ ... www.hbcse.tifr.res.in/jrmcont/notespart1/node28 .htm [21] Christos Stergiou and Dimitrios Siganos “Neural networks, 1943.

particular problem in AI is known as knowledge base. In AI for specific domain there is a knowledge base supported by various techniques for representing the knowledge. The representation can be procedural or declarative. Here we have discussed the two types of declarative methods i.e. semantic network and fuzzy logic (production system).

REFERENCES [1] John F. Sowa, “Encyclopedia of Artificial Intelligence”, Wiley, 1987, second edition, 1992. [2] E. Rich and K. Knight , Artificial Intelligence, Second Edition, McGraw-Hill Book company, 1991. [3] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third edition, Prentice Hall, 2009 [4] Baldwin, J.F. (1979), “Fuzzy logic and fuzzy reasoning”, International Journal of ManMachine Studies, vol.11, 4, 1979, pp.465-480. [5] Esragh, F. and Mamdani, E.H. (1981), “A general approach to linguistic approximation in Fuzzy Reasoning and Its Applications”, Academic Press, International Publisher, London. [6] Fox, J. (1981), “Towards a reconciliation of fuzzy logic and standard logic”, International Journal of Man-Mach. Stud., Vol.15, 1981, pp.213-220. [7] Zadeh, L.A. (1965), “Fuzzy Sets”, Proceeding of Information and Control, vol.8, 3, 1965, pp.338353. [8] Zadeh, L.A. (1975), “The Concept of a Linguistic Variable and its Application to Approximate Reasoning”, Proceeding of Information Sciences, vol.8, 1975, pp.199-249.

4

Annexure 1 Table 1: Various types of Semantics Nets S no

1

Type of semantic net/Properties Definitional networks

Invented by

year

Definition

Diagram

References

Peter (Spain)

1329

It is used to represent the is-a relation between a concept type and a newly defined subtype. Also called a generalization or subsumption network. Used to represent the property of inheritance. The information in these networks are assumed to be true.

[1], [14]

These are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some Assertional networks have been proposed as models of the conceptual structures for underlying natural language semantics. Executable network include some mechanism, such as marker passing or attached procedures, which can perform inferences, pass messages, or search for patterns and associations.

[1], [14]

2

Assertional networks

Charles Sanders Peirce

1880

3

Executable networks

Otto Selz

(1913, 1922)

[1]

A Data flow diagram

5

S no 4

Type of semantic net/Properties Learning networks

Invented by

Warren McCulloch ,Walter Pitts

year

1943

Definition

Diagram

These networks build their representations by acquiring knowledge from examples. The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights, associated with the nodes and arcs

References

[1], [2], [21]

A neural network 5

Implicational networks

Chuck Rieger

1976

These type of networks use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences. That way it is also called belief networks, causal networks, Bayesian networks, or truthmaintenance system.

6

Hybrid networks

Brachman .

1983

These combines two or more of the previous techniques, either in a single network or in separate, but closely interacting networks.For eg Krypton a hybrid network which is made by the combination of definitional network based on KL-ONE with an expert system that used a linear notation for asserting rules and facts.

[1], [2]

network for reasoning about wet grass [1], [14]

UML diagram for semantic reasoning

6