Course Name: Knowledge Engineering

Course Code:S2293241

Semester: 5

Credit: 2

Program: Computer science

Course Module: Optional

Responsible: Jianrong Wang

E-mail: wjr@tju.edu.cn

Department: Tianjin International Engineering Institute

Time Allocation (1 credit hour = 45 minutes)

Course Description

Knowledge constitutes an integral part of intelligent decision making. People make various decisions about what to do based on what they know. Knowledge Engineering plays a pivotal role in integrating human knowledge into computer systems for intelligent decision making.

This course is optional courses designed for Engineering Master of Computer Science in TIEI, and it covers the fundamental concepts, methods, techniques, and tools related to knowledge Engineering, and applies them to the building of intelligent systems that aid human decision-making.

Prerequisite

• Not required

Course Objectives

The objectives of this course are to:

• Illustrate the fundamental concepts of knowledge Engineering,

• Present various methods, techniques, and tools of Knowledge Acquisition, Knowledge Representation, and Knowledge Management,

• Illustrate essential skills necessary for the design of intelligent decision making systems (e.g., expert systems, recommender systems), and to

• Demonstrate the key issues surrounding Knowledge Engineering and Recommender Systems.

CourseSyllabus

• Introduction to Knowledge Engineering

• Introduction to Prolog

• Knowledge-based Systems: e.g., Task Ontology and

• Knowledge Acquisition: e.g., Knowledge automatic acquisition model

• Knowledge Representation and Reasoning

• Prolog Programming

• Semantic Knowledge Representation

• Introduction to Recommender Systems

• Collaborative Recommendation

• Content-based and Knowledge-based Recommendation Approaches

• Hybrid Recommendation Approaches

• Evaluating Recommender Systems

Textbooks & References

• Rudi Studer A, V. Richard Benjamins B'c, Dieter Fensel A, et al.Knowledge Engineering: Principles and Methods. Data & Knowledge Engineering, 1998, 25(97): 161–197.

• Kendal S L and Creen M.An introduction to knowledge engineering. Springer London, 2007.

• Jannach D, Zanker M, Felfernig A, et al.Recommender systems: an introduction. Cambridge University Press, 2010.

• Xiang Y, Pathan M, Tao X, et al.Data and Knowledge Engineering. Data and Knowledge Engineering, Springer, Berlin, 2012.

Capability Tasks

CT1: To know the fundamental concepts of knowledge Engineering.

CT2: To understand the various methods, techniques, and tools of Knowledge Acquisition, Knowledge Representation, and Knowledge Management.

CT3: To master the essential skills necessary for the design of intelligent decision making systems (e.g., Expert Systems, Recommender Systems).

CS1: To master the basic theories of knowledge Engineering and understand the key issues surrounding Knowledge Engineering and Recommender Systems.

CS2: To gain a comprehensive and solid foundation of essential skills for the design of intelligent decision making systems.

Achievements

• To know the concepts in fundamental Knowledge Engineering. - Level: N

• To understand the various methods, techniques, and tools of Knowledge Acquisition, Knowledge Representation, and Knowledge Management. - Level: M

• To understand the key issues surrounding Knowledge Engineering and Recommender Systems. - Level: A

• To develop an Expert System using Pro-Log. - Level: M

Students: Computer science,Year 3