This document discusses knowledge-based systems (KBS), including:
- KBS deal with unstructured knowledge and can justify decisions and learn.
- Developing KBS is difficult due to high costs, limited expert availability, and risky investments.
- A common KBS development model involves requirements, design, implementation, testing, and knowledge acquisition in multiple rounds.
- Knowledge acquisition involves eliciting, representing, and updating knowledge from domain experts.
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Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
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The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
2. Nature Of Knowledge-Based Systems
Quite different from other computer based
information systems
Deals with knowledge and works at an unstructured
level
Can justify there decision and have the ability to learn
Prepared By: Ashique Rasool
3. Difficulties in KBS Development
High cost and effort
Dealing with experts
Experts are often rare so it is difficult to meet them and take knowledge
for the system
The nature of knowledge
As the knowledge is specific to the domain, it can not be shared
without the presence of expert even the knowledge is available
The level of risk
It is some how risky because the development cost is very high and the
cost goes higher and higher in maintaining these systems
Prepared By: Ashique Rasool
5. KBS Development Model
This Development model is based on the system life
cycle. The major stages of this model are:
Elicitation of feasible requirements
Strategy Selection and Overall Design of KBS
Ontology Selection and knowledge representation
System Development and Implementation
Testing, Implementation and Training
Knowledge Acquisition
In the figure development round one just gives a
prototype and round two gives complete system
development.
Prepared By: Ashique Rasool
7. Knowledge Acquisition…
Knowledge Eliciation
The knowledge acquisition process in which the domain expert is the
only source of knowledge
Steps Of Knowledge Acquisition
Step I : Find suitable expert and knowledge engineer
Step II : Proper homework and planning
Step III : Interpreting and understanding the knowledge
provided by the experts
Step IV : Representing the knowledge provided by the
experts
Prepared By: Ashique Rasool
8. Techniques for Knowledge Acquisition
Literature review
Interview and protocol analysis
Protocol analysis is a kind of interview in which the domain expert is
asked not only to solve the problem but also to think aloud while doing
so.
Surveys and Questionnaires
Useful in gather quantitative factual knowledge (explicit knowledge)
Observations
Observing experts in a live environment gives a better picture of the
solution strategy
Diagram-Based Techniques
Process-flow diagram, conceptual maps, event and state charts
Generating Prototypes
Concept sorting
Prepared By: Ashique Rasool
9. Concept Sorting
It is a psychological technique that is useful in tapping an
organization's knowledge.
Steps of Concept Sorting
1.
2.
3.
4.
5.
Consider a textbook or ask domain expert for the basic
concepts and standards of the domain and codify each
major concept in separate cards
Arrange these cards into various groups according to
their use
Ask question to the domain expert regarding the order
and placement of the concept cards
Steps 2 & 3 are repeated until the expert is finished
answering questions or sufficient knowledge is
acquired
If the expert runs out of knowledge then the enginer
takes any three cards and ask the relationship.
Prepared By: Ashique Rasool
10. Sharing Knowledge
Experts can share meaningful outcomes of their learning
process to enrich and generalize their knowledge.
Following are the methods for knowledge sharing:
Problem Solving
Talking and story telling
Supervisory style
Prepared By: Ashique Rasool
11. Issues with Knowledge Acquisition
Most knowledge rests with experts so can not be
extracted directly
Continuously changing nature of knowledge
Difficult to prepare the experts for knowledge
acquisition process
Sometimes the knowledge are subcontious
An expert is not always correct
No single expert know everything
Opinions among multiple experts may differ
significantly
Prepared By: Ashique Rasool
12. Updating knowledge
The knowledge base in a KBS undergoes continuous
updating. Following are the three means by which
updates can be made
Self-Updating:
The system learns from the cases it handles(self learning)
Manual updates by knowledge engineer
Manual Updates by experts
Prepared By: Ashique Rasool
13. Knowledge Representation
Knowledge components should be represented in
such a way that the operations storage, retrieval,
inference and reasoning are facilitated without
disturbing the required characteristics of
knowledge
Knowledge Structure:
Prepared By: Ashique Rasool
14. Characteristics of efficient
knowledge representation facility
It should be able to represent the given knowledge
to a sufficient depth
Should preserve the fundamental characteristics of
knowledge(complete, accessible, consistent etc).
Should be able to infer new knowledge
Should be able to provide reasoning and
explanation
Should be able to store updates and support
incremental development
Should be independent enough to be reused
Prepared By: Ashique Rasool
15. Types Of Knowledge
Knowledge representation is broadly classified in
two categories
Factual Knowledge Representation
Constants
Variables
Functions
Predicates
Well-formed Formulas
First Order Logic
Procedural Knowledge Representation
Prepared By: Ashique Rasool
16. Factual Knowledge Representation
Factual knowledge are known as formal knowledge and can
be represented using first order logic supporting
constants, variables functions and predicates
Constants:
Those
symbols
that
don’t
change, represent fixed knowledge
Variables: Takes different values within a fixed
domain
Functions: Set of instructions that carry out process
and return a predefined value
Predicates: Special functions that return only
Boolean value
Well-Formed Formulas: String of symbols that is
generated by a formal language
Prepared By: Ashique Rasool
17. Factual Knowledge Representation
First Order Logic: Generated by combining predicate
logic and propositional logic.
Examples
Constants: Mohammad, Salem etc.
Variables: Man
Functions: Elder(Mohammad, Salem) returns value
Predicates: Mortal(Salem) returns Boolean value
Well-Formed Formulas: If you don’t exercise you will
gain weight. Represented as
∀x[{Human(x) ^ ~ ∃Exercise(x)} => Gain_Weight(x)]
Prepared By: Ashique Rasool
18. Representing Procedural Knowledge
Procedural knowledge represents how to reach a solution in
a given situation. Examples of procedural knowledge are:
Production Rules: Knowledge is represented as a
sequence of condition and the appropriate actions
If<condition>, then <action>
Rules are simple and easy to understand, implement and
modify. Large number of rules are required to solve simple
problems. This large volume creates problem in
documenting and encoding into the knowledgebase.
Deduction process works as follows:
Knowledge in the form of facts and rules
New facts are added
Combining the new facts with existing facts and rule
Prepared By: Ashique Rasool
19. Representing Procedural Knowledge
Semantic Networks: Graphical description of knowledge
composed of nodes (objects or concepts) and links that
show hierarchical relationships. The links carries semantic
information such as is-a, type-of, part-of etc.
Example:
Prepared By: Ashique Rasool
20. Representing Procedural Knowledge
Frames: Frames are the description of conceptual and
default knowledge about a given entity.
A frame organizes knowledge according to cause-andeffect relationships
The slots of a frame contains items like
rules, facts, videos, references etc.
It also contains pointers to other frames or procedures.
A slot is further divided into facets. A facet may be any of
the following
Example:
Explicit or default values
A range of values
An if-added type of
procedural attachment.
Name:
Broad Category:
Sub Category:
Cost:
Capacity:
Speed:
Prepared By: Ashique Rasool
Power bike
Land vehicle
Gearless
$350
Two persons
160 km/hour
21. Representing Procedural Knowledge
A frame based interpreter must be capable of the following:
Check for a slot value that is correct and within specified
range
Dissemination of definition values
Inheritance of default values
Computation of the value of a slot as required
Checking whether the correct values has been computed
Prepared By: Ashique Rasool
22. Representing Procedural Knowledge
Scripts: Script is a knowledge representation structure for
a specific situation.
It contains slots such as objects, their roles, entry and exit
conditions and different scenes describing a process in
detail.
Example:
Prepared By: Ashique Rasool
23. Representing Procedural Knowledge
Hybrid Structures: It encorporates more than one
representation scheme.
Example:
Prepared By: Ashique Rasool