Modeling with Rules Using Semantic Knowledge Engineering 1st Edition by Grzegorz Nalepa 3319666541 9783319666549

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ISBN-10 :  3319666541 

ISBN-13 : 9783319666549

Author:  Grzegorz J. Nalepa 

This book proposes a consistent methodology for building intelligent systems. It puts forward several formal models for designing and implementing rules-based systems, and presents illustrative case studies of their applications. These include software engineering, business process systems, Semantic Web, and context-aware systems on mobile devices. Rules offer an intuitive yet powerful method for representing human knowledge, and intelligent systems based on rules have many important applications. However, their practical development requires proper techniques and models – a gap that this book effectively addresses.

Modeling with Rules Using Semantic Knowledge Engineering 1st Table of contents:

Part1
1 Rules as a Knowledge Representation Paradigm
1.1 Rules in AI
1.2 Selected Knowledge Representation Methods
1.3 Expert Systems and Rules
1.4 Formal Calculi for Rules
1.5 Introduction to Attributive Logics
1.6 Rule Languages and Formalization
1.7 Summary
References
2 Knowledge Engineering with Rules
2.1 Knowledge Acquisition and Rule Base Modeling
2.2 Automated Inference with Rules
2.3 Structure of Rule Bases
2.4 Knowledge Base Analysis
2.5 Rule Interchange and Interoperability
2.6 Architectures for Integration
2.7 Summary
References
3 Selected Applications of Rules
3.1 Business Rules Approach
3.2 Rules in Business Process Management
3.3 Rules on the Semantic Web
3.4 Rules in Context-Aware Systems
3.5 Rules in Software Engineering
3.6 Rules as Programming Paradigm
3.7 Summary
References
Formal Models for Rules
Part2
4 Formalism for Description of Decision Rules
4.1 Attributive Logic with Set Values over Finite Domains
4.2 Inference in ALSV(FD) Formulae
4.3 Formulation of XTT2 Rules
4.4 Structure of XTT2 Rule Base
4.5 Inference Control in Structured Rule Bases
4.6 Formalized Detection of Anomalies in Rules
4.7 Summary
References
5 Formalized Integration of Rules and Processes
5.1 Formal Description of BPMN Process Model
5.2 General Business Logic Model
5.3 SKE-Based BPMN Process Model
5.4 Description of Example Using the Model
5.5 Summary
References
6 Prototyping Structure of Rule Bases
6.1 Main Concepts of ARD+
6.2 ARD+ Method Formalization
6.3 Prototyping Structure of the Rule Base
6.4 Design of Business Processes with Business Rules
6.5 Summary
References
7 Handling Uncertainty in Rules
7.1 Uncertainty in Mobile Systems
7.2 Improving Uncertainty Handling
7.3 Modified Certainty Factors Algebra for XTT2 Rules
7.4 Certainty Factors on Table Level
7.5 Time-Parametrised Operators for XTT2
7.6 Probabilistic Interpretation of XTT2 Models
7.7 Summary
References
Practical Studies in Semantic Knowledge Engineering
9 Semantic Knowledge Engineering Approach
9.1 Introduction to the Approach
9.2 Design Process for Rule-Based Systems
9.3 Architecture of Rule-Centric Applications
9.4 Overview of the HaDEs+ Tool Framework
9.5 Visual Design of a Modularized Rule Base
9.6 HMR Rule Language
9.7 HeaRT and HeaRTDroid Rule Engines
9.8 Rule Analysis with HalVA
9.9 Summary
References
12 Using Rules to Support Software Testing
12.1 Software Unit Testing with Rules
12.2 Decision Table-Based Testing
12.3 Framework for Test Unit Generation
12.4 Evaluation of the Testing Approach
12.5 Summary
References
13 Integrating Business Process Models with Rules
13.1 Motivation and Challenges
13.2 Design and Execution Using the Integrated Approach
13.3 Model-Based Perspective
13.4 Complexity Assessment
13.5 Practical Application of the Model
13.6 Evaluation of the Example
13.7 Summary
References
14 Rule-Based Systems and Semantic Web
14.1 Integrating SKE with the Semantic Web
14.2 DAAL Rule Language
14.3 Hybrid Reasoning with Pellet-HeaRT
14.4 DAAL in Practice
14.5 Summary
References
15 Collaborative Knowledge Engineering with Wikis
15.1 Semantic Knowledge Engineering in Loki
15.2 Case Study Implementation in Loki
15.3 Collaborative Rule Authoring with SBVR
15.4 Collaborative Modeling of BPMN Models
15.5 Summary
References
16 Designing Robot Control Logic with Rules
16.1 Robot Prototyping with Mindstorms NXT
16.2 PlNXT Library
16.3 Rule-based Control with XTT2 and HeaRT
16.4 Examples of Control Algorithms
16.5 Summary
References
17 Rules in Mobile Context-Aware Systems
17.1 Challenges for Context-Aware Systems on Mobile Platforms
17.2 Overview of the KnowMe Architecture
17.3 Knowledge Modeling in KnowMe
17.4 KnowMe Toolset
17.5 Feedback Loop in System Development
17.6 Evaluation Studies
17.7 Summary

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