Knowledge Engineering

Course Title: Knowledge Engineering

Course No: CACS458

Nature of the Course: Theory + Lab

Semester: 8

Full Marks: 20 + 20 + 60

Pass Marks: 8 + 8 + 24

Credit Hours: 3

Course Description

This course offers detailed concept about knowledge representation, logic, reasoning and principles. It includes introduction, knowledge acquisition, knowledge representation and reasoning. It does not entirely focus on theoretical concept but also strongly focuses on practical skill based learning.

Course Objectives

The general objectives of this course are to provide theoretical as well as practical knowledge of knowledge engineering to make students capable of analysis, design, implementing and managing of knowledge engineering in their personal as well professional life.

Course Contents

1. Introduction

6 hrs

1.1. Overview of data, Information and knowledge

1.2. Knowledge engineering and Knowledge management

1.3. Artificial intelligence use in knowledge Engineering

1.4. Knowledge based system and its applications

2. Knowledge Acquisition

8 hrs

2.1. Information gathering

2.2. Information retrieval

2.3. Applications of Natural Language processing

Morphology, lexicon, syntax and semantics

Parsing, POS tagging, named entity tagging

3. Machine Learning

12 hrs

3.1. Machine Learning and its applications

3.2. Supervised and unsupervised learning

3.3. Classification and clustering

3.4. Classification algorithms

Linear classifiers

nearest neighbor

Support Vector Machines

Decision tree

Random forest

Neural networks

Case based reasoning

4. Knowledge representation and reasoning

7 hrs

4.1. Proposition logic, predicate logic and reasoning

4.2. Knowledge representation languages

4.3. Non-monotonic reasoning

4.4. Probabilistic reasoning

5. Ontology Engineering

6 hrs

5.1. Overview to Ontology

5.2. Classifications of ontology

5.3. Methodology use in Ontology

5.4. Ontology VS Language

6. Knowledge Sharing

9 hrs

6.1. Information Distribution and Integration

6.2. Semantic web and its applications

RDF and linked data

Description logic

Web Ontology language

6.3. Social web and semantics

Laboratory Works

The practical work consists of all features of knowledge engineering and case studies.

1.Knowledge Engineering Features and Case Studies

Text Books

1.Kendal, Simon, Creen, Malcolm, An Introduction to Knowledge engineering, Springer first edition, 2007
2.R.J. Brachman and H.J. Levesque. Knowledge representation and resoning (Elsevier 2004)

Reference Books

1.Stuart Russell and Peter Norvig, Artificial Intelligence: A modern approach (Prentice Hall edition, second edition, 2002)
2.P. Jackson, Introduction to expert systems, Addison Wesley, 1999.
3.John Debenham, Knowledge Engineering: Unifying Knowledge Base and Database Design, Springer, 1998

Notes:

This syllabus follows the official BSC-CSIT curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.

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