Knowledge Engineering 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.
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 Units
6 Units · 48 Hrs
Overview of data. Information and knowledge
Knowledge engineering and Knowledge management
Artificial intelligence use in knowledge Engineering
Knowledge based system and its applications
Information gathering
Information retrieval
Applications of Natural Language processing
Morphology, lexicon, syntax and semantics
Parsing, POS tagging, named entity tagging
Machine Learning and its applications
Supervised and unsupervised learning
Classification and clustering
Classification algorithms
Linear classifiers
Nearest neighbor
Support Vector Machines
Decision tree
Random forest
Neural networks
Case based reasoning
Proposition logic, predicate logic and reasoning
Knowledge representation languages
Non-monotonic reasoning
Probabilistic reasoning
Overview to Ontology
Classifications of ontology
Methodology use in Ontology
Ontology VS Language
Information Distribution and Integration
Semantic web and its applications
RDF and linked data
Description logic
Web Ontology language
Social web and semantics
Laboratory Works
The practical work consists of all features of knowledge engineering and case studies.
Teaching Methods
The teaching faculties are expected to create environment where students can update and upgrade themselves with the current scenario of computing and information technology with the help of topics listed in the syllabus. The general teaching pedagogy that can be followed by teaching faculties for this course includes class lectures, laboratory activity, group discussions, case studies, guest lectures, research work, project work, assignments (Theoretical and Practical), and written and verbal examinations.
References & Books
Kendal, Simon, Creen, Malcolm, An Introduction to Knowledge engineering, Springer first edition, 2007
R.J. Brachman and H.J. Levesque. Knowledge representation and reasoning (Elsevier 2004)
Reference Books
Stuart Russell and Peter Norvig, Artificial Intelligence: A modem approach (Prentice Hall edition , second edition, 2002)
P. Jackson, Introduction to expert systems, Addison Wesley, 1999.
John Debenham, Knowledge Engineering: Unifying Knowledge Base and Database Design, Springer, 1998.