Alor-Hernández / Valencia-García Current Trends on Knowledge-Based Systems

1. Auflage 2017
ISBN: 978-3-319-51905-0
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 120, 290 Seiten

Reihe: Intelligent Systems Reference Library

This book presents innovative and high-quality research on the implementation of conceptual frameworks, strategies, techniques, methodologies, informatics platforms and models for developing advanced knowledge-based systems and their application in different fields, including Agriculture, Education, Automotive, Electrical Industry, Business Services, Food Manufacturing, Energy Services, Medicine and others.

Knowledge-based technologies employ artificial intelligence methods to heuristically address problems that cannot be solved by means of formal techniques. These technologies draw on standard and novel approaches from various disciplines within Computer Science, including Knowledge Engineering, Natural Language Processing, Decision Support Systems, Artificial Intelligence, Databases, Software Engineering, etc.

 As a combination of different fields of Artificial Intelligence, the area of Knowledge-Based Systems applies knowledge representation, case-based reasoning, neural networks, Semantic Web and TICs used in different domains.

 The book offers a valuable resource for PhD students, Master’s and undergraduate students of Information Technology (IT)-related degrees such as Computer Science, Information Systems and Electronic Engineering.

Autoren/Hrsg.

Alor-Hernández, Giner

Valencia-García, Rafael

Inhaltsverzeichnis

1;Preface;6
2;Acknowledgements;11
3;Contents;12
4;Contributors;14
5;List of Figures;17
6;List of Tables;21
7;Semantic Web Applications;23
8;1 im4Things: An Ontology-Based Natural Language Interface for Controlling Devices in the Internet of Things;24
8.1;Abstract;24
8.2;1.1 Introduction;25
8.3;1.2 Related Works;26
8.4;1.3 im4Things System;28
8.4.1;1.3.1 im4Things App;31
8.4.1.1;1.3.1.1 Bot Configuration;31
8.4.1.2;1.3.1.2 Instant Messaging;31
8.4.2;1.3.2 im4Things Cloud Service;32
8.4.2.1;1.3.2.1 im4Things API;32
8.4.2.2;1.3.2.2 Communication Management;32
8.4.2.3;1.3.2.3 Security;33
8.4.2.4;1.3.2.4 Understanding Module;33
8.4.3;1.3.3 im4Things Bot;35
8.4.3.1;1.3.3.1 Conversational Agent;35
8.5;1.4 Evaluation and Results;37
8.5.1;1.4.1 Subjects;37
8.5.2;1.4.2 Procedure;38
8.5.3;1.4.3 Results;38
8.5.4;1.4.4 Discussion;40
8.6;1.5 Conclusions and Future Research;41
8.7;Acknowledgements;42
8.8;References;42
9;2 Knowledge-Based Leisure Time Recommendations in Social Networks;44
9.1;Abstract;44
9.2;2.1 Introduction;44
9.3;2.2 Related Work;46
9.4;2.3 Social Networking, Semantics and QoS Foundations;48
9.4.1;2.3.1 Influence in Social Networks;48
9.4.2;2.3.2 Leisure Time Places Semantic Information and Similarity;49
9.4.3;2.3.3 Physical Distance-Based and Thematic-Based Location Similarity;51
9.4.4;2.3.4 Leisure Time Places QoS Information;52
9.4.5;2.3.5 User’s Profile for Enabling Recommendations;52
9.5;2.4 The Leisure Time Recommendation Algorithm;54
9.6;2.5 Experimental Evaluation;58
9.6.1;2.5.1 Determining the Number of Influencers;59
9.6.2;2.5.2 Estimating the Taxonomy Level of Places Categories of Interest per User;60
9.6.3;2.5.3 Interest Probability Threshold;61
9.6.4;2.5.4 Recommendation Formulation Time;64
9.6.5;2.5.5 User Satisfaction;65
9.7;2.6 Conclusions and Future Work;66
9.8;References;67
10;3 An Ontology Based System for Knowledge Profile Management;70
10.1;Abstract;70
10.2;3.1 Introduction;71
10.3;3.2 Theoretical Background;71
10.4;3.3 State of the Art;74
10.4.1;3.3.1 Ontologies for Knowledge Management;74
10.4.2;3.3.2 Ontologies for Users’ Profile Management;75
10.5;3.4 An Ontology for Knowledge Profile Management;76
10.5.1;3.4.1 Development of the Ontology;77
10.5.1.1;3.4.1.1 Specification;77
10.5.1.2;3.4.1.2 Conceptualization;77
10.6;3.5 Results;83
10.6.1;3.5.1 The Case Study;84
10.6.2;3.5.2 Ontology Implementation;85
10.6.2.1;3.5.2.1 Determination of Base Components;85
10.6.2.2;3.5.2.2 Detailed Definition of Base Components;86
10.6.2.3;3.5.2.3 Normalization;87
10.7;3.6 Discussion and Conclusion;89
10.8;Acknowledgements;90
10.9;References;90
11;4 Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language;94
11.1;Abstract;94
11.2;4.1 Introduction;95
11.3;4.2 Related Work;96
11.4;4.3 Corpus;98
11.5;4.4 LIWC and Stylometric Variables;99
11.6;4.5 Machine Learning Approach;100
11.7;4.6 Experiment;101
11.7.1;4.6.1 Combination of LIWC Dimensions and Stylometric Dimension;101
11.7.2;4.6.2 Text Analysis with LIWC and WordSmith;101
11.7.3;4.6.3 Training a Machine Learning Algorithm and Validation Test;102
11.8;4.7 Evaluation and Results;103
11.8.1;4.7.1 Results for the Tourism Corpus;103
11.9;4.8 Discussion of Results;107
11.9.1;4.8.1 Comparison;109
11.10;4.9 Conclusion and Future Work;111
11.11;Acknowledgements;111
11.12;References;111
12;Knowledge Acquisition and Representation;114
13;5 Knowledge-Based System in an Affective and Intelligent Tutoring System;115
13.1;Abstract;115
13.2;5.1 Introduction;116
13.3;5.2 Related Work;116
13.4;5.3 Fermat Architecture and ITS Knowledge Representation;118
13.4.1;5.3.1 The System Archetypes;118
13.4.2;5.3.2 Fermat’s Architectural Style;119
13.4.3;5.3.3 Intelligent Tutoring System into Fermat Social Network;120
13.4.4;5.3.4 Knowledge Representation in the Intelligent Tutoring System;121
13.5;5.4 Recognizing Emotional States;125
13.5.1;5.4.1 Artificial Neural Network for Recognizing Emotional States;125
13.5.1.1;5.4.1.1 Training and Testing the Network with Emotional States;125
13.6;5.5 The Decision Network;126
13.6.1;5.5.1 Integrating Affect into the ITS;127
13.7;5.6 Evaluation and Results;129
13.7.1;5.6.1 Fermat Real-Time Evaluation;129
13.8;5.7 Conclusions and Future Directions;131
13.9;Acknowledgments;132
13.10;References;132
14;6 A Software Strategy for Knowledge Transfer in a Pharmaceutical Distribution Company;134
14.1;Abstract;134
14.2;6.1 Introduction;134
14.3;6.2 Theoretical Background;136
14.3.1;6.2.1 Knowledge and Knowledge Management;136
14.3.2;6.2.2 Knowledge Transfer;137
14.4;6.3 Software Strategy;138
14.4.1;6.3.1 Identification;140
14.4.1.1;6.3.1.1 Knowledge Identification;140
14.4.1.2;6.3.1.2 Knowledge Assessment;141
14.4.2;6.3.2 Capture/Storage;141
14.4.2.1;6.3.2.1 Capture of Missing Knowledge;141
14.4.2.2;6.3.2.2 Knowledge Repository;141
14.4.3;6.3.3 Transfer/Visualization;141
14.4.3.1;6.3.3.1 Enablers;142
14.4.3.2;6.3.3.2 Enabling Tools;142
14.4.4;6.3.4 Application;142
14.4.4.1;6.3.4.1 Usage Assessment;142
14.5;6.4 Software Strategy Implementation;143
14.5.1;6.4.1 Identification;144
14.5.1.1;6.4.1.1 Knowledge Identification;144
14.5.1.2;6.4.1.2 Knowledge Assessment;145
14.5.2;6.4.2 Capture/Storage;146
14.5.2.1;6.4.2.1 Knowledge Repository;146
14.5.3;6.4.3 Transfer/Visualization;147
14.5.3.1;6.4.3.1 Enabling Processes and Tools;147
14.5.4;6.4.4 Application;149
14.5.4.1;6.4.4.1 Usage Assessment;150
14.6;6.5 Conclusions;152
14.7;References;153
15;7 GEODIM: A Semantic Model-Based System for 3D Recognition of Industrial Scenes;156
15.1;Abstract;156
15.2;7.1 Introduction;156
15.3;7.2 State of the Art;157
15.3.1;7.2.1 Object Recognition;158
15.3.2;7.2.2 Semantic on Object Recognition;159
15.4;7.3 GEODIM Overview;161
15.4.1;7.3.1 Process of Geometric Primitives Recognition;162
15.4.2;7.3.2 Semantic Enrichment Process;164
15.4.3;7.3.3 Semantic Model;168
15.5;7.4 A Real Use Case: Objects Recognition in an Industrial Facility;169
15.5.1;7.4.1 Recognition Process of Geometric Primitives;171
15.5.2;7.4.2 Semantic Enrichment Process;171
15.6;7.5 Evaluation;174
15.7;7.6 Conclusions and Future Work;176
15.8;Acknowledgements;176
15.9;References;176
16;8 Beyond Interoperability in the Systems;179
16.1;Abstract;179
16.2;8.1 Introduction;179
16.3;8.2 The Challenge of Interoperability in the Industry 4.0;182
16.4;8.3 Related Work;184
16.5;8.4 The Advanced Industrial Technical Interoperability Concept (TIC);188
16.5.1;8.4.1 Introduction;188
16.5.2;8.4.2 Description of the TIC Layer;191
16.5.3;8.4.3 Basic Interoperability Functions;194
16.5.4;8.4.4 Application of the TIC Layer to Manage Complex Design Reuse Scenarios;195
16.6;8.5 Discussion of the TIC Layer in Industry 4.0 Toolchains;197
16.7;8.6 Conclusions and Future Work;199
16.8;Acknowledgements;199
16.9;References;199
17;Knowledge-Based Decision Support Systems (Tools for Industrial Knowledge Management);202
18;9 Knowledge-Based Decision Support Systems for Personalized u-lifecare Big Data Services;203
18.1;Abstract;203
18.2;9.1 Introduction;203
18.3;9.2 Related Work;204
18.4;9.3 Proposed Platform;205
18.4.1;9.3.1 Data Acquisition and Management;206
18.4.2;9.3.2 Data Wrangling;206
18.4.2.1;9.3.2.1 Data Cleansing;207
18.4.2.2;9.3.2.2 Data Transformation;208
18.4.2.3;9.3.2.3 Data Loading;208
18.4.3;9.3.3 Big Data Storage and Processing;209
18.4.4;9.3.4 Learning Models;209
18.4.5;9.3.5 Model Interface;210
18.4.6;9.3.6 Knowledge Bases;210
18.4.7;9.3.7 Reasoner and Inferencing Services;210
18.4.8;9.3.8 Analytical Services;211
18.4.9;9.3.9 u-Lifecare Services API;211
18.5;9.4 Case Study;211
18.6;9.5 Conclusion;217
18.7;References;218
19;10 Decision Support System for Operational Risk Management in Supply Chain with 3PL Providers;220
19.1;Abstract;220
19.2;10.1 Introduction;220
19.3;10.2 State of the Art;221
19.3.1;10.2.1 Supply Chain Risk Management—SCRM;222
19.3.2;10.2.2 Operational Risk;223
19.3.3;10.2.3 Outsourcing as a Logistics Strategy;223
19.3.4;10.2.4 Risk in 3PL Operations;224
19.4;10.3 Proposed Model;225
19.4.1;10.3.1 Risk Identification;226
19.4.2;10.3.2 Risk Prioritization;227
19.4.3;10.3.3 Risk Quantification;227
19.4.4;10.3.4 Risk Management;228
19.5;10.4 Case Study;228
19.5.1;10.4.1 Risk Identification;229
19.5.2;10.4.2 Risk Prioritization;229
19.5.3;10.4.3 Risk Quantification;232
19.5.4;10.4.4 Risk Management;233
19.6;10.5 Concluding Remarks;234
19.7;References;234
20;11 Assessment of Ergonomic Compatibility on the Selection of Advanced Manufacturing Technology;238
20.1;Abstract;238
20.2;11.1 Introduction;239
20.3;11.2 Literature Review;239
20.3.1;11.2.1 Models for Assessment and Selection of AMT;239
20.3.2;11.2.2 Applications of Fuzzy Logic in Manufacturing;240
20.3.3;11.2.3 Fuzzy Inference;241
20.3.4;11.2.4 Axiomatic Design for the Assessment and Selection of AMT;243
20.4;11.3 Methodology;244
20.4.1;11.3.1 Methods;244
20.4.2;11.3.2 Mathematical Model;248
20.4.3;11.3.3 Results Using a Numerical Example;249
20.4.4;11.3.4 Conclusions and Future Research;252
20.5;Acknowledgements;253
20.6;References;253
21;12 Developing Geo-recommender Systems for Industry;255
21.1;Abstract;255
21.2;12.1 Introduction;256
21.3;12.2 State of the Art;256
21.3.1;12.2.1 Recommender Systems in Different Domains;257
21.3.2;12.2.2 Geographic Information Systems Applied to Environmental and Urban Studies;258
21.4;12.3 How to Develop a Geographic Recommender System?;261
21.4.1;12.3.1 Development Tools for Recommender Systems;264
21.5;12.4 Usage Scenarios of Geographic Recommender Systems;266
21.5.1;12.4.1 Geo-recommendations for Selecting Points of Sale (Pos);267
21.5.2;12.4.2 Geo-recommendations for Product Deliveries;268
21.5.3;12.4.3 Touristic Geo-recommendations;268
21.5.4;12.4.4 Geo-recommendation for Public Transportation;269
21.5.5;12.4.5 Geo-recommendations for Sale Offers Within a Specific Radius;269
21.5.6;12.4.6 GEOREMSYS: A Geo-recommender System for Selecting POS;270
21.6;12.5 Conclusions;274
21.7;Acknowledgements;274
21.8;References;275
22;13 Evaluation of Denoising Methods in the Spatial Domain for Medical Ultrasound Imaging Applications;277
22.1;Abstract;277
22.2;13.1 Introduction;277
22.3;13.2 Review of Speckle Noise Reduction Methods for Ultrasound Medical Images;278
22.3.1;13.2.1 Transform Domain Methods;279
22.3.2;13.2.2 Spatial Domain Methods;280
22.4;13.3 Multiplicative Noise Model;281
22.5;13.4 Speckle Denoising Filters Compared in This Chapter;282
22.5.1;13.4.1 Average Filter;282
22.5.2;13.4.2 Median Filter;283
22.5.3;13.4.3 Frost Filter;283
22.5.4;13.4.4 Kuan et al. Filter;284
22.5.5;13.4.5 Lee Filter;284
22.5.6;13.4.6 Gamma MAP Filter;285
22.5.7;13.4.7 Anisotropic Diffusion;285
22.5.7.1;13.4.7.1 Speckle Reducing Anisotropic Diffusion (SRAD);286
22.6;13.5 Metrics;287
22.7;13.6 Results;287
22.8;13.7 Conclusions;291
22.9;Acknowledgements;291
22.10;References;291
23;Appendix A: Attributes for AMT Ergonomic Evaluation;296
24;Appendix B: Rates Given by Experts to Milling Machines Alternatives;299
24.1;References;301