Multi-Encrypted Extended Hierarchical Censored Production Rules: An Implementation
Author: Deepa Chaudhary N.K Jain Monica Singh
Abstract
This paper exhibits multi-encrypted representation of knowledge employed in EHCPRs System.
In all intelligent agents one of the points of consideration is to know that how the knowledge is
structured, how it is viewed by designers, and what effective method of organizing the
knowledge structures in memory have been used. Knowledge representation in a multi encrypted
fashion will enhance learning; give better understanding, imagination and feel of real world
objects (concepts) that are encapsulated in Multi-Encrypted EHCPR knowledge base. The
implementation of Multi-encrypted Extended Hierarchical Censored Production Rule system
represents a multi-encrypted structure for knowledge of both the types- tacit and explicit
knowledge. This project is a web application which is a browser oriented interface. Being a web
application it can have the scope of being online in future work.
Keywords
EHCPR Multi-Encryption Concepts Tacit Knowledge Auto-Extraction Instances Supervised
Learning
Introduction
Karl Wiig (1996) defines knowledge as “the insights, understandings and the practical
know-how that we all possess.”
Knowledge is the information which represents long term relationships i.e., ways of doing
things, common sense, ideas, methods, skills, and so on. Knowledge is the backbone of Artificial
Intelligence and so issues related to knowledge representation, understanding, knowledge
designing and implementation are relevance. In the creation of common sense knowledge base
major information contents are the ontology of classes, instances and individuals; parts,
properties, and materials of objects; functions and uses of objects; location, duration, post and
preconditions of events; behaviour; emotions; strategies; and context. To accommodate all these
concepts in a single unit, a unique knowledge representation scheme is required.
Definitions of tacit and explicit knowledge provide a proper classification for all the existing
knowledge. Tacit knowledge is the form of knowledge that is subconsciously understood and
applied, difficult to articulate, developed from direct experiences and action and usually shared
through highly interactive conversation and shared experiences. While tacit knowledge appears
to be simple, it has far reaching consequences and is not widely understood. Tacit knowledge is
not easily shared. Although it is that which is used by all people, it is not necessarily able to be
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easily articulated. It consists of beliefs, ideals, values, schemata and mental models which are
deeply ingrained in us and which we often take for granted. While difficult to articulate, this
cognitive dimension of tacit knowledge shapes the way we perceive the world. In the field of
knowledge management, the concept of tacit knowledge refers to a knowledge possessed only by
an individual and difficult to communicate to others via words and symbols. Therefore, an
individual can acquire tacit knowledge without language. Apprentices, for example, work with
their mentors and learn craftsmanship not through language but by observation, imitation, and
practice. The key to acquiring tacit knowledge is experience. Without some form of shared
experience, it is extremely difficult for people to share each other’s thinking processes. Tacit
knowledge is difficult to transfer to another person by means of writing it down or verbalizing it.
For example, stating to someone that we are the students of computer is a piece of explicit
knowledge that can be written down, transmitted, and understood by a recipient. However, the
ability to explore and use any complex equipment requires all sorts of knowledge that is not
always known explicitly, even by expert practitioners, and which is difficult to explicitly transfer
to users.
Taking this into account now Artificial Intelligent system requires:
1. Transformation of tacit knowledge into explicit form of knowledge.
2. The use of some knowledge representation structure to capture the knowledge which
creates declarative knowledge.
The richer the representation, the more useful it is for subsequent problem solving. Both humans
and computers must be able to react promptly to new information, and they must be able to
change or repair their knowledge when new information produces contradictions or when initial
assumptions are withdrawn. An intelligent system should exhibit the capability to acquire fresh
knowledge through its regress interaction with the external world in a given working
environment. The knowledge flow in the intelligent system is through human being who
represents the knowledge in some formalism as frames, logic, semantic net, grid and production
rule etc. or suitably combines some of these schemes and hence is more effective in subsequent
use. In this interaction between the system and human, there must be some fundamental gap in
the understanding of the knowledge. This hindrance, in the understanding of the knowledge, is
due to different nature/functionality of different formalisms employed. This problem can be
overcome when the knowledge representation [1] model employed is made to be functionally
equivalent to all the standard representation schemes. Moreover, it is useful to convert all the
existing knowledge into this functionally equivalent knowledge representation scheme.
Semantic nets [2] represent the meaning of sentences by means of a formal network structures.
Various forms of these network structures are possible; but typically objects are represented by
nodes in a network and their relations by arcs between the nodes. Semantic network is very
expressive formalism in terms of inheritance hierarchy, multiple inheritance and representation
of exceptions. Frame [3] is a general record like structure which represents an entity, or a
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general concept as a set of slots (attributes) and associated values. Unlike a record, structure or
class, it is possible to add slots to a frame dynamically i.e., at run time of a program execution
and the contents of the slot need not be simple value. Frames are implicitly associated with one
another because the value of a slot can be another frame. Frames suggest top down mechanism
to represent general high level knowledge. Predicates [4], the language of logic, are another
way of representing knowledge. Predicates can be used to illustrate all the basic concepts of
logic. The atomic sentences (indivisible syntactic elements) consist of a single predicate
followed by a parenthesized list of terms. The meaning of a concept comes from the ways in
which it is connected to other concepts while its specificity stays constant. A Censored
Production Rule CPR [5] is written as: IF P THEN D UNLESS C. Where P is production rules,
D is decision and C is censor or exceptional condition.
The “rule + exception” models [6] provide a realistic description of real world and hierarchies
provide managing structure to the world knowledge base with different knowledge levels.
Hierarchical Censored Production Rule system (HCPR) [7] combines rules, exceptions and
hierarchy in this scheme of knowledge representation.
With the inclusion of new operators, the HCPR system generalized further with the name
Extended Hierarchical Censored Production Rules (EHCPRs) given by N.K Jain and K.K
Bharadwaj [8].The Extended Hierarchical Censored Production Rules (EHCPRs) System ([9],
[10], [11], [12], [13-22]) is an intelligent system which employs an EHCPR as a unit for
representing real world knowledge. The EHCPRs system uses EHCPRs to create declarative
knowledgebase to capture the knowledge from different environments.
Different current projects in Artificial Intelligence such as Treasure, CYC, WorldNet, etc. are
successful to represent extensively large knowledgebase. These systems have utilized multiple
representation schemes to represent different types of concepts. In place of employing multiple
representation schemes, the EHCPRs system uses a general representation by means of EHCPRs
for all existing knowledge world. In an EHCPR, there are various operators to define different
relations or dependencies of the objects (or concepts) with other objects (or concepts). Here is the
definition of an EHCPR [17] [20]:
A
{decision/concept/object} /*As Head
of rule*/
If
B[b1, b2, …, bm]
{preconditions (AND conditions)}
Unless
C[c1, c2, …, cn]
{censor conditions (OR conditions)}
Generality G
{general concept}
Specificity S[a1,a2, …, ak]
{specific concepts}
/* mutually
exclusive set*/
Has-Part
[Part_Concept1: (Default),(Constraints), Part_Concept2:
(Default), (Constraints), …… , Part_Conceptp: (Default),(Constraints)]
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Has-Property
[Property_Concept1:
(Default),
(Constraints),
Property_Concept2: (Default), (Constraints), ……, Property_Conceptq:
(Default),(Constraints)]
Has_Instance [ …]
{instances}
: γ, δ
………………………. (1)
Here ‘A’ is a concept, consequent or decision part in the EHCPR. ‘B’ is the antecedent or
precondition part and contains the defining features of concept A. When ‘B’ is satisfied, it leads
to take the action or decision given by ‘A’. ‘C’ is censor (or exception) part of the ‘If-ThenUnless’ rule. The specificity information with ’S’ in an EHCPR is the clue about the next set of
more specific entities. The general information with ‘G’ in an EHCPR is the clue about the next
general entity related to the entity ‘A’ up in the hierarchy. The ‘Has-Part’ and the ‘Has-Property’
operators relegates with the characteristic features, which normally holds true but also at the
same time are allowed to be false for an item, or individual who is an instance of that particular
concept, in an extraordinary situation. The ‘Has_Instance’ operator represents the list of distinct
individual, example, or instance, of that particular concept. Parameter ‘γ’ is a numeric measure of
strength of “If” relationship between ‘A’ and ‘B’. ‘γ’ is referred to as the 0–level strength of
implication. Every censor (c1, c2, …,cn) is associated with an estimate of its likelihood (δ1,…,
δn) also called the certainty factor of that censor.
‘δ’ = γ + summation of all δ’s
…………………….(2)
and is referred to as the 1–level strength of implication. A detailed discussion of ’γ’ and ’δ’ is
given in ([23],[1]). Their value is constrained to be greater than 0.5 and less than 1 for a
meaningful and hence useful implication.
Each concept represented as an EHCPR can have various instances and all instances of EHCPRs
are represented uniformly through the same general form given below (4):
Head
/* particular instance of a concept / name of individual object*/
Instance-of (a general concept)
Has-Part (set of actual known parts)
Has-Property (set of known true properties)
…………….. (4)
Here, Head is the name of the instance. Instance-of is the name of the concept, of which Head is
an instance. The override or peculiar attributes of an instance are kept with its Has-Part and HasProperty operators. Other attributes might be inherited by it through ‘Instance-of’ and
subsequently by the ‘Generality’ operator.
Multi-Encrypted EHCPRs System
A concept is an abstraction to symbolize the world knowledge. A concept is either an entity or a
process. An entity may be physical or abstract. Something formed in the mind; a thought or
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notion, an idea derived or inferred from specific instances or occurrences is a concept. Any
structure, system, plan, living being, and non-living is a concept. A general idea of a concept is
created by abstracting, drawing, most common and uncommon characteristics. For example, the
abstract general idea or concept that is designated by the word "red" is that characteristic which is
common to apples, cherries, and blood. The abstract general idea or concept that is signified by
the word "cow" is the collection of those characteristics which are common to all existing
different breeds of cows. A better learning of any concept can be presumed with the use of all the
existing encryptions for a concept. Uses of all the types of encryptions make the path of learning
easy. When a mother teaches her kid about a cow she shows him picture, mimic cow’s moos,
shows him how a cow moves like that. A kid uses his senses and creates his understanding for
this concept i.e. for cow. His mother told him what to call this entity in his native language and
as the child grows his learning make him capable to differentiate between different cows, their
common and different treads. The ME-EHCPRs system has its senses in the form of all the
encryptions to learn a concept. An EHCPR structured in [14] is encapsulating all types of
encryptions to increase the knowledge domain of any concept.
This ME-EHCPR system is further enhanced in this paper with a new concept of representing
and teaching the knowledge base using multiple- encryption forms called Multi-Encrypted
EHCPR system.
The Multi-Encrypted EHCPR system structure will take the form:
1. “A {Decision/ Concept/Object}” is a concept, consequent or decision part in the MultiEncrypted EHCPR.
2. “B {preconditions (AND Conditions)}”. When B is satisfied, it leads to take the action or
decision given by A. These are the concepts define concept A.
3. “C {Censor conditions (OR condition)}”. C is censor (or exception) part of the ‘If –ThenUnless’ rule. It is a set of disjunction of all the censor conditions. For a Production rule
certainty increases with the falsehood of censor conditions and decreases without
checking these. If we assume for a certain concept that all censor conditions are known to
us and all are false. It automatically boosts hundred percent certainty for the concept
existence.
4. “S {Specificity Concept (mutually exclusive set)}”. S is the specificity information
which gives a clue about the next set of more specific entities. Systematic arrangement of
available knowledge is desirable feature for its manipulation and use. This operator
suggests tree structure organization for all the ME-EHCPRs. It reduces repetition of
inherited knowledge also.
5. “G {General Concept}”. G is the general concept which gives knowledge of the concept
ancestor/ general to the concept ‘A’ i.e. up in the hierarchy.
6. “TE {Textual Encryption}”. TE is the textual encryption which is a set of names of the
concept A in multiple languages. In this paper we are considering only one language. It is
an initiation node/pointer to refer all the related concepts in a particular language.
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7. “PE {Picture Encryption}”. PE is the pictorial encryption which is a set of pictures of
the concept A.
8. “AE {Audio Encryption}”. AE is the audio encryption which is a set of the sounds of the
concept A. “VE {Video Encryption}”. VE is the video encryption which is a set of videos
of the concept A.
In the Notebooks 1914-16 Wittgenstein writes
The difficulty of my theory of logical portrayal was that of finding a connection between the
signs on paper and a situation outside in the world. I always said that truth is a relation
between the proposition and the situation, but could never pick out such a relation (19e-20e;
quoted in Word and World, 71).
“A picture is worth a thousand words". This idea conveys use of audio and video encryption
in ME-EHCPRs System. It also aptly characterizes one of the main goals of visualization.
Block diagram of ME-EHCPR System showing functional components of the system to exhibit
the system capability as an Artificial intelligent agent is shown in fig.1
Multi-Encrypted (Declarative Knowledge)
Data Base (Set of Instances)
Knowledge Base (Set of EHCPRs)
Context Sensitive Multi-Lingual User Interface
Learning
Reasoning
(Procedural Knowledge)
Fig1: Block diagram of ME-EHCPR
1. Multi-Encrypted Knowledge Base and ME-Data Base, where knowledge base consists of
set of ME-EHCPR’s and data base consists of set of instances of ME-EHCPR’s present in
knowledge base. ME-EHCPRs KB is organized in tree structure. As a prototype we are
handling only one KB. There will be many more interactive KBs [23] organized in Tree
structure too. A concept defined in one KB can be used in others too. Ex: Red colour, red
light, red dress, and red car these all will access concept “red” from the same concept
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red. As knowledge base is permanent it will auto extract permanent knowledge from
database too (discussed in logical description and shown in implementation).
2. Context Sensitive Multi-Lingual User Interface. Context sensitivity can be applied at
various levels in an Artificial Intelligent system. Context sensitivity in location and
cultural aspect can have a significant value in the area of International system [13]. It can
be applied to representation, user interface, reasoning, and learning. Context sensitive
approach of designing a User Interface will result in intelligent interface. An intelligent
system senses the mood of the user and able to do some of the work of its own without
the intervention of the user working in the System. This interactive nature of the System
will result in making the user more enthusiastic, active and innovative while working
with the System. Thus by implementing context sensitivity property we are actually
making a machine intelligent artificially. By Multi-Lingual we mean the system will
respond, act, interact and connect with the external world in the user’s own languages.
Practically speaking it will be a multi lingual system. Not only user interface but the
whole system can have a multi lingual approach that can be considered in future work.
3. Learning: Capability of automatic restructuring of the knowledge base and to view the
fresh knowledge in the context of its already acquired knowledge is central to an effective
learning system. Improvement in a task performance may be regarded as a consequence
of knowledge improvement. Learning is a process of increasing an agent’s knowledge.
Such a process will typically involve conducting various forms of inference, validating
their results, and memorizing the results for future use.
4. Reasoning: The reasoning process in ME-EHCPRs system is more flexible in comparison
to semantic networks and frames as it facilitates both backward and forward rule chaining
while semantic networks use bottom up representation and frame suggested top down
mechanism. This ME-EHCPRs System exhibits efficiency to work with all possible
logical reasoning. A few are tested [24-26] and some are under process. The reasoning
process in EHCPR system is more flexible in comparison to semantic networks and
frames as it facilitates both backward and forward rule chaining while semantic networks
use bottom up representation and frames suggested top down mechanism.
Significance of Multi Encryption
The concept that exists in knowledge base does have meaning in real world aspect. Their
existence matters to other concepts also with whom these concepts have relations in the terms of
generality, specificity etc. So the selection of operators or primitives is the most crucial task.
When we are saying that a concept is defined and characterized by if, censor, generality,
specificity, has-part and has-property operators it means that all world knowledge (concepts) will
contribute through operators. But these representations are textual, that is the knowledge
maintained in the knowledge base is in the form of textual encryption. Encryption in this context
means the textual explanation of representing the knowledge about a concept. Encryption means
various ways, forms of representing knowledge so that it can give better understanding,
imagination and feeling of real world concepts. To make the knowledge base natural looking and
natural behaving only textual encryption of representing knowledge is not enough some more
encryption techniques needed to be augmented in the current system of EHCPRs. For this we
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have used pictorial, visual, audible encryption techniques. For example the concept ‘Human’ can
be explained using pictures of Human like ‘A human drinking water; A human driving car; A
human laughing; A human surfing internet; A human lying on bed while ill’. These pictures of a
concept can make the system understand various perspective of Human being as a concept in real
world. By visualizing these pictures we can also analyse various activities done by human being,
various emotions a human can have, and various circumstances a human can face. So through
pictorial encryption we can come to know a number of new things about a human being which is
complex to be represented in textual manner. Further various specialty about the human being
concept can also be understood like ‘Some Human are good writer; some are good dancer; some
are good singer’. So things are getting easier to be explained and understood using these
encryption techniques. Pictorial representations of EHCPR are to show various activities,
emotions, circumstances, events, decisions about a concept. Visual or video encryption technique
of representing knowledge is to show how these activities are actually done; how these emotions
are actually made; how these circumstances arises; how events triggered; how decisions are
taken. So visual encryption actually answers the question of “How’s”. It sequences number of
events while performing a task. For example ‘A Human drinking water’ will have number of
steps while drinking. First of all it will take a glass, fill it with water from a jug containing water.
Then it will hold the glass tightly and lift it towards its mouth. Then it will sip an amount of
water that can be accommodated by its mouth. Then ultimately it will swallow the intake. This
whole procedure of drinking water can be explained in such a detail using visual encryption
technique. Pictorial encryptions are two-dimensional while visual encryption is threedimensional. A two-dimensional picture communicates only one point of view and it cannot
indicate size, shape, surface or volume. These are the most essential visual attributes. Shape, size
and colour are the major attributes that can be better defined using pictorial and visual
encryption. Shape and size of a concept is always crucial. The effect made on viewer is so
different that the viewer can make any similarity as well as differentiation among other concepts
present in knowledge base or real world. For example, one can compare the size of a human with
a plant. One can make a conclusion by this comparison that plants are shorter than human being.
Colour also plays a vital role in understanding the concept more efficiently. Colour attribute
always matter since it influences the way we look at a concept. A concept may have multiple
colours like concept human can be fair, black or whitish. Further the concept parrot can be of
colour Green, Blue, Red, and Yellow. These varieties of concept parrot can be considered as
various instances or various specificity (hyacinth macaw, scarlet macaw, sun conure) of the
concept Parrot. The colour of a concept may also change over time. Like the concept Mango
changes its shape, size and colour over its life time. Initially it grows in the form of flower, and
then they grow and change its shape like a fruit and green in colour, after some time it ripens and
grows bigger in size and get orange or red in colour. All these explanation about a particular
concept require to be memorized. For memorizing a thing listening is an important aspect. We
can memorize only when we can feel anything. By listening to the behaviour of a concept we can
better understand and feel its scope and relativity with present environment. Listening to a fact
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leads to feeling the fact which further leads to the imagination of a brain which may go beyond
the scope. By audio clips of describing various facts about a concept we can give a different view
of understanding and learning. For example the concept Bird can have audio or sound encryption
of a bird chirping in the trees, bird flapping their wings in a wish to fly can make the listener
better understand and memorize the very concept. It can further make differentiation among
related concepts like Birds do have a censor condition of airplane. Airplane flies but it is not a
Bird. So the ‘fly’ property of both the concept has a completely different aspect. So by visual and
audible encryption of ‘fly’ property of both the concept can be differentiated clearly and
conveniently. Therefore it has been concluded here that any concept being a common property
among different concept can behave and operate in different manner.
ME-EHCPR Logical Description of the Implementation
This project is implemented using 4-tier architecture to ensure security of code. 4 tier architecture
means there is layering of code in the project and they will work by linking each another. In our
project 1st layer is User Interface; through which user will interact with the system, 2 nd layer is
Knowledge Object; this will create the knowledge object as a packet and encapsulate whole
information about a concept or entity into an object called knowledge object, 3rd layer is
Knowledge Application Layer; This layer will act as an adapter to pass the knowledge object to
the knowledge access layer for further processing, 4th is Knowledge Access Layer; This layer
will invoke the respective member function to establish the connection with the knowledge base
and send the request along with the knowledge object to the knowledge base via the very channel
created by it. Once these processing have been done and signal approaches the knowledge base,
the knowledge base then further upgrade itself using respective procedures. This project is a web
application which is a browser oriented interface. Being a web application it can have the scope
of being online in future work. The web applications do have application level configuration
information which is maintained in ‘web.config’ file. This file maintains information like
authorization, connection string etc. The connection string holds the information about the
database server. So this information is confidential. To confirm its security this information is
stored in encrypted form using RSA algorithm.
Automatic extraction of various concepts when a particular concept is generated
Apart from security issues, which are one of the most significant parts of this project, one more
issue is the deserving candidate of that concern, and that is automatic concept extraction upon a
particular concept generation. Whenever we think about a concept a lot more thing comes into
mind which can be acting as another new entity and so a new concept. While explaining any
concept we can have several operators to describe it in a structural way called EHCPR. Each bite
of information present in the EHCPR packet to represent the knowledge about the concept can
also be considered as a concept. Like all set of If conditions, censor condition, specificity,
generality, has-part, has-property an EHCPR packet have are also another concept.
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So while feeding knowledge about a particular concept in the knowledge base, we are actually
discovering a number of new concepts resulting in cloud of concepts. Let us come back to the
definition of concept.
A Concept in this context may be defined as anything which is capable of holding any
information from which knowledge can be extracted and correlated with other concepts in the
domain. Concepts allow us to store useful and pertinent conceptual information that applies to a
set of individuals and use this knowledge in the future. Without categories and the conceptual
knowledge associated with them, we would have to learn all the information of all the objects we
ever confront separately, but storing all the information of all the objects encountered is
impossible for a finite thing such as a brain or a computer. So we can conclude here that every
piece of knowledge about particular concept is a concept as well. Practically it means each set of
operator’s attributes that are possessing information in EHCPR structure are further treated as a
new concept by the system if they are not existing already. Let us explore furthermore our
previous example of BIRD.
Bird
If:
{“Bipedal”, ”Fly”, “Chirp”, “Warm Blooded”}
Unless:
Dinosaur
Generality:
Animal
Specificity:
Crow, Ostrich, Parrot, Penguin, Sparrow, Kiwi, Eagle
Has_Part:
{Legs, Wings, Beak, Teeth}
Has_Property: {Live in nest, Eat grains}
Has_Instance: {Titu, Mithu, Sweety}
When we are putting this knowledge packet of concept in the knowledge base, the system will
auto extract a number of new concepts present in the operator’s very knowledge packet other
than instances (see figure 5). Like if the user is entering knowledge about the concept ‘BIRD’ a
number of concepts will be extracted from its ME-EHCPR packet like ‘Bipedal’, Constant body
temperature, lay eggs, animal, crow, ostrich, parrot, penguin, sparrow, kiwi, Eagle etc. are auto
extracted by the present ME-EHCPR and upgraded in the knowledge base. Meanwhile these new
extracted concepts will have incomplete information in their ME-EHCPR structure but in future
more information about respective concept in the form of ME-EHCPR attributes can be upgraded
into the knowledge base. It means the next time we are accessing the knowledge base we can
search those concepts and add on further information related to it in order to complete it as an
ME-EHCPR structure.
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Since instances are represented as individual object/ occurences of the concept. set of instances
are maintained in database other than knowledge base as we have discussed in the block diagram
of Multi-Encrypted EHCPR system. Instances do have their life time duration, i.e. their existence
in database is not permanent. Knowledge base will contain concept knowledge and instances
those status has been changed into permanent. Ex; one human called Johny is an instance and so
is in database as long as he died and then transferred into KB. Important point is:
Instances of any concept will be stored in database but other than textual encryption will also
enrich the existing data base automatically as it works for the concept as it is.
ME-EHCPR Implementation Detail
Implementation of multi-encrypted EHCPRs system is done using Micro Soft Visual Studio 2010
Dot Net Framework, C# language, ASP.Net Web Tool, AJAX Control Toolkit. The knowledge
base is maintained in SQL Server 2008 R2.
This project is having proper login system, a user has to login with their password in order to
access the system.
Fig 2: login user interface
For new user there is a proper registration system which will take some of the essential details of
user and register it with the system.
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Fig 3: Registration User Interface
Next is knowledge packet ME-EHCPR Input User Interface. From here the authenticated user
only will upgrade the knowledge base by putting knowledge in the operators of the ME-EHCPR
given.
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Fig 4: User Interface for an authenticate user having knowledge base access permission
After that we have an interface to search the concept. Here in the figure below we can see the
learning property where multiple concepts are appearing which is auto extracted by the system.
From here the user can search the respective concept to get the knowledge about it.
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Fig 5: end user searching concept
Note: We enter only one concept of Bird and the system takes all the knowledge as other
concepts once from all the operators. Hierarchy will be maintained through generality and
specificity. It is one kind of auto extraction of knowledge. Another is when we give an
instance and its all encryptions other than textual will update the existing ME-EHCPR and
if no ME-EHCPR exists than knowledge will remain intact with instance in Data base.
In the Concept view user interface, the concept will appear in the ME-EHCPR structure. In the
button ‘Play Video’, ‘Play Audio, ‘Show Picture’ different encryption will be played of the
respective concept that user have searched.
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Fig 6: end user searched concept view
Conclusion
Knowledge management is the management of information, skills, experience, innovation
and intelligence. ME-EHCPRs system is a conceptual knowledgebase system with the
ability to return search result based on the user’s request, request history and subject
matter experience. Lots of work is going on to make this system more efficient in capturing,
storing, sharing and enhancing the knowledge with a systematic, holistic approach to the
sustainable improvement of the handling of knowledge and within the knowledge which
may create new knowledge, learn knowledge with context sensitivity.
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