Download A Tour Towards Knowledge Representation Techniques

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'.
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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.
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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
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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
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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.
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