Workman Semantic Web

Implications for Technologies and Business Practices
1. Auflage 2016
ISBN: 978-3-319-16658-2
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 234 Seiten

This book examines recent developments in semantic systems that can respond to situations and environments and events. The contributors to this book cover how to design, implement and utilize disruptive technologies. The editor discusses the two fundamental sets of disruptive technologies: the development of semantic technologies including description logics, ontologies and agent frameworks; and the development of semantic information rendering and graphical forms of displays of high-density time-sensitive data to improve situational awareness. Beyond practical illustrations of emerging technologies, the editor proposes to utilize an incremental development method called knowledge scaffolding -a proven educational psychology technique for learning a subject matter thoroughly. The goal of this book is to help readers learn about managing information resources, from the ground up and reinforcing the learning as they read on.

Michael D. Workman, Ph.D. is a Professor of Computer Information Systems and Human Factors with the College of Engineering, School of Computing at the Florida Institute of Technology.

Autoren/Hrsg.

Workman, Michael

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Contributors;9
4;Chapter-1;11
4.1;Introduction to This Book;11
4.1.1;1.1 Resource Description Framework;11
4.1.2;1.2 Ontology Markup;13
4.1.3;1.3 Agent Frameworks;14
4.1.4;1.4 Looking Forward;15
4.1.5;References;15
5;Chapter-2;16
5.1;Semantic Cognition and the Ontological to Epistemic Transformation: Using Technologies to Facilitate Understanding;16
5.1.1;2.1 Introduction;16
5.1.1.1;Structure, Structuration, and Agency;16
5.1.1.2;Agency and Agent Systems;18
5.1.1.3;Goal-Directed Agents;19
5.1.1.4;The Problem of Meaning;20
5.1.2;2.2 Cognition Overview;21
5.1.2.1;Memory and Cognition;21
5.1.2.2;Information Structure and Semantics;23
5.1.3;2.3 Visual Perception;25
5.1.3.1;Vision and Visual Perception;25
5.1.3.2;Visual Memory Processing;26
5.1.4;2.4 Memory and Attention;27
5.1.4.1;Working Memory;28
5.1.4.2;Types of Memory;29
5.1.4.3;Cognitive Processing;30
5.1.4.4;Semantic Relatedness and Cognition;33
5.1.4.5;Semantic Priming;34
5.1.4.6;Content and Technical Accuracy;35
5.1.5;2.5 Summary;36
5.1.6;References;36
6;Chapter-3;40
6.1;Using Symbols for Semantic Representations: A Pilot Study of Clinician Opinions of a Web 3.0 Medical Application;40
6.1.1;3.1 Introduction;40
6.1.2;3.2 Theory Foundations and Design Principles;41
6.1.3;3.3 Method;43
6.1.3.1;Participants and Preparations;43
6.1.3.2;Instrumentation;44
6.1.3.3;Procedures;45
6.1.4;3.4 Results;45
6.1.5;3.5 Discussion;46
6.1.6;References;47
7;Chapter-4;48
7.1;Emerging Semantic-Based Applications;48
7.1.1;4.1 Background;50
7.1.2;4.2 Semantics Behind Keywords;53
7.1.2.1;Semantic Keyword-Based Search: QueryGen;54
7.1.2.2;Semantic Data Retrieval: Doctopush;60
7.1.3;4.3 Semantic Information Extraction: GENIE;63
7.1.4;4.4 Technologies for Fuzzy Knowledge;67
7.1.4.1;Modeling Fuzzy Ontologies with Fuzzy OWL 2;70
7.1.4.2;Reasoning with Fuzzy Ontologies Using fuzzyDL;72
7.1.4.3;Reasoning with Fuzzy Ontologies Using DeLorean;73
7.1.5;4.5 Applying Semantic Web Technologies to Mobile Computing;74
7.1.5.1;Semantic Location Granules;75
7.1.5.2;Semantic Management of LBSs: SHERLOCK;80
7.1.6;4.6 Discussion;85
7.1.7;References;87
8;Chapter-5;93
8.1;Semantics: Revolutionary Breakthrough or Just Another Way of Doing Things?;93
8.1.1;5.1 Introduction;93
8.1.1.1;The Data World Today: Relational Plus;94
8.1.1.2;What Would be Better? And by what Criteria?;96
8.1.1.3;What We Mean by Semantic Technology?;97
8.1.1.4;Foundational Concepts in Modeling;99
8.1.1.5;Importance of Similarity;102
8.1.1.6;Importance of Model Theory;102
8.1.1.7;Logic and Logic Programming as Reference;103
8.1.1.8;RDF, RDFS, and OWL as Semantic Languages;104
8.1.1.9;Evaluation of Semantic Web Languages Against Criteria;107
8.1.2;5.2 How Are Semantic Web Languages Different?;108
8.1.2.1;Comparison with and Evaluation of XML;108
8.1.2.2;Comparison and Evaluation of Object-Oriented Languages;110
8.1.2.3;Relational Revisited: Comparison and Evaluation;112
8.1.2.4;Comparison and Evaluation of UML with Notes on MOF, eMOF, ODM, and SMOF;116
8.1.2.5;Summary of Representations;118
8.1.3;5.3 Semantics in Real-World Solutions;119
8.1.3.1;Model-Driven Equipment Overhaul: Common Understanding;119
8.1.3.2;Data Provenance: Information Availability;120
8.1.3.3;Early Manufacturability: Inference;121
8.1.3.4;Smart Grid: Interoperability;122
8.1.3.5;Data Science: Data Speaks;123
8.1.4;5.4 Conclusion;124
8.1.5;References;124
9;Chapter-6;127
9.1;Unnatural Language Processing: Characterizing the Challenges in Translating Natural Language Semantics into Ontology Semantics;127
9.1.1;6.1 Introduction;127
9.1.2;6.2 Levels of NL Semantics;129
9.1.3;6.3 Morphological Level;129
9.1.4;6.4 Lexical Level;134
9.1.5;6.5 Syntax-Level;139
9.1.6;6.6 Summary and Value Proposition;141
9.1.7;References;142
10;Chapter-7;144
10.1;The Lexical Bridge: A Methodology for Bridging the Semantic Gaps Between a Natural Language and an Ontology;144
10.1.1;7.1 Introduction;144
10.1.2;7.2 Technical Approach: The LB;145
10.1.2.1;Lexicalized Ontology Example;145
10.1.2.2;LB Components;146
10.1.2.3;Building the LB;147
10.1.2.4;Potential Applications of the LB;148
10.1.2.5;RDF Redundancy Definition;149
10.1.2.6;LB Methodology Applied to RDF Redundancy Evaluation;150
10.1.2.7;LB Architecture;152
10.1.2.8;LB Prototype and Results;153
10.1.3;7.3 Conclusion and Next Steps;157
10.1.4;References;158
11;Chapter-8;159
11.1;Reliable Semantic Systems for Decision Making: Defining a Business Rules Ontology for a Survey Decision System;159
11.1.1;8.1 Structure of Researched Data;159
11.1.2;8.2 Applying Semantic Technologies to a Survey Decision Support System;162
11.1.2.1;Analysis of Questions and Responses;162
11.1.2.2;Analysis of Secondary Data and Business Rules;164
11.1.2.3;Requirements and Data Analysis Techniques;165
11.1.2.3.1;Decision Support Systems;166
11.1.2.3.2;Requirements Engineering;167
11.1.2.3.3;Visual Data Analysis;169
11.1.2.3.4;Business Rules Analysis;171
11.1.2.3.5;Semantic Analysis;172
11.1.3;8.3 Pattern Analysis and Validation Techniques;172
11.1.4;8.4 Architecture of a Semantic Decision Domain;174
11.1.5;8.5 Conclusion;176
11.1.6;References;177
12;Chapter-9;179
12.1;University Ontology: A Case Study at Ahlia University;179
12.1.1;9.1 Introduction;179
12.1.2;9.2 University Ontology;180
12.1.2.1;Building the Ontology;180
12.1.2.2;Specification;181
12.1.2.3;Property and Relationship;182
12.1.2.4;Implementation;184
12.1.2.5;Verification;184
12.1.3;9.3 Case: Ahlia University Ontology;185
12.1.3.1;Conclusion;188
12.1.4;References;189
13;Chapter-10;190
13.1;Semantic Enrichment of Event Stream for Semantic Situation Awareness;190
13.1.1;10.1 Introduction;190
13.1.2;10.2 Overview on Event Processing;191
13.1.2.1;Finite-State Machine;192
13.1.2.2;Graph-Based Approaches;193
13.1.2.3;Rule-Based Approaches;193
13.1.2.4;RETE Algorithm in Event Processing;194
13.1.2.5;Storage-Based Event Processing;195
13.1.2.6;Data Stream Processing Systems;196
13.1.3;10.3 Semantic Complex Event Processing;197
13.1.3.1;Issues and Challenges;198
13.1.3.2;Applications of Knowledge-Based Event Processing;201
13.1.3.3;Example Use Case: High-Level Market Monitoring;201
13.1.3.4;Approaches for Knowledge-Based Event Processing;204
13.1.3.5;Stream Reasoning and Resource Description Framework Processing Systems;205
13.1.4;10.4 Fusion of Event Stream and Knowledge;206
13.1.4.1;Semantic Enrichment;209
13.1.4.2;Plan-Based Semantic Enrichment;210
13.1.4.3;Planning Multistep Event Enrichment and Detection;212
13.1.5;10.5 Summary and Discussion;212
13.1.6;References;213
14;Chapter-11;218
14.1;Semantic Web and Business: Reaching a Tipping Point?;218
14.1.1;11.1 Placing the Web in Context;218
14.1.1.1;A Review of Traditional Semantic Technology Application to Business;220
14.1.1.2;Historical Understanding of Business Applications Tied to the Semantic Web;222
14.1.1.2.1;Data Annotation;223
14.1.1.2.2;Search;224
14.1.1.2.3;Social Media Analysis;224
14.1.1.2.4;Knowledge Management;224
14.1.1.2.5;Data Integration;225
14.1.1.3;Semantic Web Technologies are Not Keeping Up;225
14.1.1.4;Trends that Could Tip the Scale;226
14.1.1.4.1;Larger and Larger Shopper Profiles;227
14.1.1.4.2;Omni-channel Marketing;228
14.1.1.4.3;Personalization;229
14.1.1.4.4;Real-Time and Contextual Communication;229
14.1.1.4.5;The Future of Semantic Technologies and Today’s Marketing Challenge;230
14.1.1.4.6;Ontology Extraction;231
14.1.1.4.7;Personal Ontology Development;231
14.1.1.4.8;Personal Ontology Fusion;231
14.1.1.4.9;Reasoning and Simulation for Consumer;232
14.1.1.4.10;Agent-Based Frameworks;232
14.1.2;11.2 Conclusions;233
14.1.3;References;233