Understanding Semantics-Based Decision Support
- Length: 140 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2020-11-26
- ISBN-10: 0367443139
- ISBN-13: 9780367443139
- Sales Rank: #0 (See Top 100 Books)
This book is an attempt to establish in the readers the importance of creating interoperable data stores and writing rules for handling this data. It also covers extracts from a few project dissertations and a research funded project that the author had supervised.• Describes the power of ontologies for better data management• Provides an overview of knowledge engineering including ontology engineering, tools and techniques• Provides sample development procedures for creating two domain ontologies.• Depicts the utility of ontological representation in situation awareness• Demonstrates recommendation engine for unconventional emergencies using a hybrid reasoning approach.• The text explains how to make better utilization of resources when emergency strikesGraduates and undergraduates doing courses in artificial intelligence, semantic web and knowledge engineering will find this book beneficial.
Cover Half Title Title Page Copyright Page Table of Contents Foreword Preface About the Author Acknowledgment Acronyms and Abbreviations 1. Semantics-based Decision Support - An Introduction 1.1. Decision Support 1.2. Situation Awareness 1.3. Paradigm Shift from Data to Knowledge 1.4. Intelligence and Semantics 1.4.1. Understanding Semantics 1.4.2. Semantic Intelligence 1.5. Intersection of STs and DSS 1.6. Sample Use Cases 1.6.1. E-Government 1.6.2. Healthcare 1.6.3. Understanding Natural Language 1.6.4. IT Service 1.6.5. Tourism 1.6.6. Oil and Gas Industry 1.6.7. Education 1.6.8. Medicine 1.6.9. Customer Service 1.6.10. NASA 1.6.11. Law 1.6.12. News 1.6.13. Big Fish in the Market 1.7. Case Study 1.7.1. Unconventional Emergencies 1.7.2. The State of the Art 1.7.3. Managerial Implications (Benefits) 1.7.3.1. Government 1.7.3.2. Military Personnel 1.7.3.3. Society References 2. Semantic Technologies as Enabler 2.1. Data Models 2.1.1. Data Models for Structured Data 2.1.2. Data Models for Semi-Structured and Unstructured Data 2.2. Representing Semantics 2.3. Representative Semantic Data Models 2.3.1. Semantic HTML 2.3.2. Using Web (2.0) APIs 2.3.3. Publishing Linked Data 2.4. Semantic Technologies 2.4.1. Foundations 2.4.2. The Data Model (RDF) 2.4.3. Ontology 2.4.3.1 Ontology Development 2.4.3.2 Ontology Evaluation 2.4.4. Knowledge-Description Languages 2.4.4.1 RDF Schema 2.4.4.2 Web Ontology Language 2.4.4.3 Simple Knowledge Organization System 2.4.5. Serializations (Syntax/Formats) 2.4.6. Manipulating RDF Data 2.5. RDF Data Access and Management 2.5.1. RDF Data Storage 2.5.2. Query Processing 2.5.2.1 Adding, Deleting, and Exporting Data 2.5.3. Inference/Reasoning 2.5.3.1 Ontology Reasoning 2.5.3.2 Rule-Based Reasoning 2.6. Rules and Rule Languages 2.6.1. Kinds of Rules 2.6.2. Rule Languages 2.6.2.1 Discussion 2.7. Semantic Tools 2.7.1. Ontology Development Environments 2.7.2. RDF-izers 2.7.3. Application Programming Interfaces 2.7.3.1 Apache Jena 2.7.3.2 Eclipse RDF4J (Formerly OpenRDF Sesame) 2.7.3.3 Redland C Libraries 2.7.3.4 OWL API 2.7.3.5 Sparta 2.7.3.6 Protégé-OWL API 2.7.4. Semantic Repository and Reasoner 2.7.5. Semantic Reasoner 2.7.6. Ontology Visualization References 3. Semantics-Based Decision Support for Unconventional Emergencies 3.1. The Problem 3.2. The Solution 3.3. Tools and Techniques References 4. Knowledge Representation and Storage 4.1. Developing Knowledge Stores 4.2. Developing Ontologies 4.2.1. Defining Ontology 4.2.2. Methodology 4.2.2.1 Scope Determination 4.2.2.2 Concept Identification 4.2.2.3 Concept Analysis and Organization 4.2.2.4 Encoding 4.2.2.5 Evaluation 4.2.3. SupOnt-EO 4.3. Evaluation of Ontologies 4.3.1. Evaluation by Verification 4.3.1.1 Metric-Based Evaluation 4.3.1.2 Criteria-Based Evaluation 4.3.1.3 Cost-Based Evaluation 4.3.2. Evaluation by Validation 4.3.2.1 Semantic Layer 4.3.2.2 Application Layer 4.4. Archiving Past Experiences (Case Base) 4.5. Acquiring Expertise (Rule Base) References 5. Situation Awareness 5.1. System Architecture 5.2. Knowledge Base Browser 5.2.1. Visualization 5.2.2. Search 5.3. Question Answering 5.3.1. Predefined Queries 5.3.2. Custom Queries References 6. Advisory System 6.1. Conceptual Model 6.1.1. Algorithmic Overview 6.2. Case-Based Reasoning 6.2.1. Representation and Storage of Cases 6.2.2. CBR Life Cycle 6.3. Augmenting CBR with Semantic Technologies 6.3.1. Life Cycle of Ontology-Based CBR 6.4. Augmenting CBR with Decision Trees 6.4.1. Incremental CBR 6.4.2. Selecting the Most Distinctive Feature 6.5. Augmenting CBR with Rules 6.6. Ongoing Case Study 6.6.1. Action Recommendation in Earthquake References 7. Multilingual and Multimodal Access 7.1. Motivation 7.1.1. Benefits of Multilingual and Multimodal Access 7.2. Multilingual Knowledge Representation 7.2.1. Integration of Linguistic Constructs 7.2.2. Ontology Mediation 7.2.3. Globalization 7.3. Multilingual Keyword Search 7.3.1. Demonstration of Multilingual Search in Hindi 7.3.2. Demonstration of Multilingual Search in Punjabi 7.4. Multimedia Semantic Integration and Resource Access 7.4.1. Multimedia Ontology Construction 7.4.2. Multimedia Visualization 7.4.3. Multimedia Search References 8. Concluding Remarks and Outlook for the Future Index
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