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Knowledge engineering - 4GMC14A1

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Goals

The course presents the tools of knowledge engineering and their applications to the industry of the future.
Students will know how to extract, formalize and exploit knowledge to help make the right industrial decision.

Content

Introduction to Knowledge Engineering
Data VS. Information VS. Knowledge
Types of knowledge
Role of Knowledge Engineering for AI
Hybrid AI: monitoring, simulation, expert knowledge
Knowledge representations (frame, rules, FOL, semantic networks, Triples…)
Expert systems
Knowledge engineering VS. Knowledge management
TP. Find and decorticate a use case of KE in I.40
Ontologies
Ontologies and Linked data
Methods for capturing expert knowledge
Modelling languages RDF, RDFS, OWL, NoSQL Graph
Reasoning with inference engine (SPARQL & RDF query language)
Use cases of ontologies in industry 4.0
TP. Development of an ontology with Protégé
Case-based reasoning
Models for representing cases
Similarity measures & cases retrieval methods
Methods for reusing similar cases for decision aid
Discussion on case base updating
TP. Evaluation of a new product / a new project

Prerequisites

Students will have taken and validated the following courses: Probability and Statistics; Programming with R, Programming with Python

Tests

This weighting is compatible with the organization of distance learning courses and exams

At least 2 marks for practical work or continuous assessment: TP1 and TP2
One exam grade : E1

Grade = 0.4*((TP1+TP2)/2) + 0.6 * E1

Cette pondération est compatible avec une organisation des enseignements et des examens en distanciel

Au moins 2 notes de TP ou contrôle continu: TP1 et TP2
Une note d'examen : E1

Note = 0.4*((TP1+TP2)/2) + 0.6 * E1

Calendar

The course exists in the following branches:

  • Curriculum - Engineer student Master SCM - Semester 7
  • Curriculum - Engineer student Master PD - Semester 7
see the course schedule for 2021-2022

Additional Information

Course ID : 4GMC14A1
Course language(s): FR

You can find this course among all other courses.

Bibliography

King (2009), Knowledge Management and Organizational Learning , Springer

Kolodner Janet L. (1993), Case-Based Reasoning. M. Kaufmann

Bergmann, R., Kolodner, J., & Plaza, E. (2005). Representation in case-based reasoning. Knowledge Engineering Review, 20(3), 209-214

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59

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Date of update June 14, 2021

Université Grenoble Alpes