Web Semantics. Cutting Edge and Future Directions in Healthcare

Title-page_2021_Web-Semantics
	Web Semantics
Copyright_2021_Web-Semantics
	Copyright
Contents_2021_Web-Semantics
	Contents
List-of-contributors_2021_Web-Semantics
	List of contributors
Preface_2021_Web-Semantics
	Preface
		Representation
		Reasoning
		Security
Chapter-1---Semantic-intelligence--An-overview_2021_Web-Semantics
	1 Semantic intelligence: An overview
		1.1 Overview
			1.2 Semantic Intelligence
				1.2.1 Publishing and consuming data on the web
				1.2.2 Semantic Intelligence technologies applied within enterprises
			1.3 About the book
Chapter-2---Convology--an-ontology-for-conversational-agents_2021_Web-Semant
	2 Convology: an ontology for conversational agents in digital health
		2.1 Introduction
		2.2 Background
		2.3 The construction of convology
			2.3.1 Specification
			2.3.2 Knowledge acquisition
			2.3.3 Conceptualization
			2.3.4 Integration
		2.4 Inside convology
			2.4.1 Dialog
			2.4.2 Actor
			2.4.3 ConversationItem
			2.4.4 Event
			2.4.5 Status
		2.5 Availability and reusability
		2.6 Convology in action
			2.6.1 Other scenarios
		2.7 Resource sustainability and maintenance
		2.8 Conclusions and future work
		References
Chapter-3---Conversion-between-semantic-data-models--the-story_2021_Web-Sema
	3 Conversion between semantic data models: the story so far, and the road ahead
		3.1 Introduction
		3.2 Resource Description Framework as a semantic data model
		3.3 Related work
		3.4 Conceptual evaluation
			3.4.1 Comparison study
			3.4.2 Generalized architecture
		3.5 Findings
		3.6 Concluding remarks
		References
Chapter-4---Semantic-interoperability--the-future-of-health_2021_Web-Semanti
	4 Semantic interoperability: the future of healthcare
		4.1 Introduction
			4.1.1 Healthcare interoperability: a brief overview
		4.2 Semantic web technologies
			4.2.1 Resource data framework
			4.2.2 RDF graphs
			4.2.3 Vocabularies, RDFS and OWL
			4.2.4 SPARQL
			4.2.5 Applications of semantic web technology
		4.3 Syntactic interoperability
			4.3.1 Health level 7 version 2.x
			4.3.2 Health level 7 version 3.x
			4.3.3 Fast healthcare interoperable resource
		4.4 Semantic interoperability
			4.4.1 History of clinical coding systems
			4.4.2 Difference between clinical terminology systems and clinical classification systems
			4.4.3 Semantic interoperability and semantic web technology
		4.5 Contribution of semantic web technology to aid healthcare interoperability
			4.5.1 Syntactic interoperability and semantic web technology
			4.5.2 Semantic interoperability and semantic web technology
		4.6 Discussion and future work
			4.6.1 Challenges with the adoption of semantic web technology at the semantic interoperability level
			4.6.2 Challenges with the adoption of semantic web technology at the syntactic interoperability level
		4.7 Conclusion
		References
Chapter-5---A-knowledge-graph-of-medical-institutions-in-K_2021_Web-Semantic
	5 A knowledge graph of medical institutions in Korea
		5.1 Introduction
		5.2 Related work
			5.2.1 Formal definition of knowledge base
			5.2.2 Public data in Korea
		5.3 Medical institutions in Korea
		5.4 Knowledge graph of medical institutions
			5.4.1 Data collection
			5.4.2 Model of administrative district
			5.4.3 Model of medical institutions
			5.4.4 Graph transformation
		5.5 Conclusion
		References
Chapter-6---Resource-description-framework-based-semantic-knowl_2021_Web-Sem
	6 Resource description framework based semantic knowledge graph for clinical decision support systems
		6.1 Introduction
		6.2 Knowledge representation using RDF
			6.2.1 Knowledge-based systems
			6.2.2 Knowledge representation in knowledge-based system
			6.2.3 Resource description framework for knowledge representation
		6.3 Simple knowledge organization system
			6.3.1 Knowledge organization system
			6.3.2 Simple knowledge organization system
			6.3.3 Simple knowledge organization system core and resource description framework
		6.4 Semantic knowledge graph
			6.4.1 Knowledge graphs
			6.4.2 Semantic knowledge graph
			6.4.3 RDF-based semantic knowledge graph
		6.5 Semantic knowledge graph for clinical decision support systems
			6.5.1 Clinical decision support systems
			6.5.2 Semantic knowledge graph for clinical decision support systems
			6.5.3 Advantages of RDF-based semantic knowledge graph
		6.6 Discussion and future possibilities
		6.7 Conclusion
		References
Chapter-7---Probabilistic--syntactic--and-semantic-reasoning-u_2021_Web-Sema
	7 Probabilistic, syntactic, and semantic reasoning using MEBN, OWL, and PCFG in healthcare
		7.1 Introduction
		7.2 Multientity Bayesian networks
		7.3 Semantic web and uncertainty
		7.4 MEBN and ontology web language
		7.5 MEBN and probabilistic context-free grammar
		7.6 Summary
		References
Chapter-8---The-connected-electronic-health-record--a-semantic-e_2021_Web-Se
	8 The connected electronic health record: a semantic-enabled, flexible, and unified electronic health record
		8.1 Introduction
		8.2 Motivating scenario: smart health unit
		8.3 Literature review
			8.3.1 Background
				8.3.1.1 Electronic health record-related standards and terminologies
				8.3.1.2 Semantic interoperability: internet of things-based ontologies
			8.3.2 Related Studies
				8.3.2.1 Electronic health records and EHR systems
		8.4 Our connected electronic health record system approach
			8.4.1 Architecture description
			8.4.2 Data processing module
				8.4.2.1 Preprocessing data
				8.4.2.2 Data transformation
				8.4.2.3 Data analysis based on data aggregation process
		8.5 Implementation
		8.6 Experimental results
			8.6.1 Analysis performance of connected electronic health record
			8.6.2 Response time of connected electronic health record
		8.7 Conclusion and future works
		References
Chapter-9---Ontology-supported-rule-based-reasoning-for-emer_2021_Web-Semant
	9 Ontology-supported rule-based reasoning for emergency management
		9.1 Introduction
		9.2 Literature review
		9.3 System framework
			9.3.1 Construction of ontology
		9.4 Inference of knowledge
			9.4.1 System in action
				9.4.1.1 Tools/techniques/languages employed
			9.4.2 Sample scenarios
		9.5 Conclusion and future work
		References
Chapter-10---Health-care-cube-integrator-for-health-care-da_2021_Web-Semanti
	10 Health care cube integrator for health care databases
		10.1 Introduction: state-of-the-art health care system
		10.2 Research methods and literature findings of research publications
			10.2.1 Indian health policies and information technology
			10.2.2 Electronic health record availability in India and its privacies challenges
			10.2.3 Electronic health records databases/system study
			10.2.4 Study of existing health knowledgebases and their infrastructures
			10.2.5 Study of existing solution available for health data integration
			10.2.6 Health care processes and semantic web technologies
			10.2.7 Research objectives
		10.3 HCI conceptual framework and designing framework
		10.4 Implementation framework and experimental setup
		10.5 Result analysis, conclusion, and future enhancement of work
			10.5.1 Result analysis
			10.5.2 Conclusion
			10.5.3 Future enhancement of work
		References
Chapter-11---Smart-mental-healthcare-systems_2021_Web-Semantics
	11 Smart mental healthcare systems
		11.1 Introduction
		11.2 Classification of mental healthcare
		11.3 Challenges of a healthcare environment
			11.3.1 Big data
			11.3.2 Heterogeneity
			11.3.3 Natural language processing
			11.3.4 Knowledge representation
			11.3.5 Invasive and continuous monitoring
		11.4 Benefits of smart mental healthcare
			11.4.1 Personalization
			11.4.2 Contextualization
			11.4.3 Actionable knowledge
			11.4.4 Invasive and continuous monitoring
			11.4.5 Early intervention or detection
			11.4.6 Privacy and cost of treatment
		11.5 Architecture
			11.5.1 Semantic annotation
			11.5.2 Sentiment analysis
			11.5.3 Machine learning
		11.6 Conclusion
		References
Chapter-12---A-meaning-aware-information-search-and-retrieval_2021_Web-Seman
	12 A meaning-aware information search and retrieval framework for healthcare
		12.1 Introduction
		12.2 Related work
		12.3 Semantic search and information retrieval in healthcare
		12.4 A framework for meaning-aware healthcare information extraction from unstructured text data
			12.4.1 Meaning-aware healthcare information discovery from ontologically annotated medical catalog database
			12.4.2 Semantic similarity computation
			12.4.3 Semantic healthcare information discovery—an illustration
		12.5 Future research dimensions
		12.6 Conclusion
		Key terms and definitions
		References
Chapter-13---Ontology-based-intelligent-decision-support-syst_2021_Web-Seman
	13 Ontology-based intelligent decision support systems: A systematic approach
		13.1 Introduction
		13.2 Enabling technologies to implement decision support system
			13.2.1 IoT-enabled decision support system for data acquisition, transmission, and storage
				13.2.1.1 Data acquisition
				13.2.1.2 Data transmission and storage
			13.2.2 Application of machine learning and deep learning techniques for predictive analysis of patient’s health
				13.2.2.1 Identification of diseases
				13.2.2.2 Smart electronic health records
				13.2.2.3 Behavioral monitoring
		13.3 Role of ontology in DSS for knowledge modeling
			13.3.1 Issues and challenges
			13.3.2 Technology available
		13.4 QoS and QoE parameters in decision support systems for healthcare
			13.4.1 Why QoS versus QoE is important in such system implementation in healthcare?
			13.4.2 Definition of significant quality of service and quality of experience parameters
				13.4.2.1 Quality of service metrics parameters
				13.4.2.2 QoE metrics
		13.5 Conclusion
		References
Chapter-14---Ontology-based-decision-making_2021_Web-Semantics
	14 Ontology-based decision-making
		14.1 Introduction
		14.2 Issue-Procedure Ontology
		14.3 Issue-Procedure Ontology for Medicine
		14.4 Conclusion
		References
Chapter-15---A-new-method-for-profile-identification-using-ont_2021_Web-Sema
	15 A new method for profile identification using ontology-based semantic similarity
		15.1 Introduction
		15.2 Proposed method
			15.2.1 Weight allocation for keyword
			15.2.2 Semantic matching
				15.2.2.1 Build paths
				15.2.2.2 Semantic similarity
				15.2.2.3 Weight computing of the concept
			15.2.3 Profile creation
		15.3 Conclusion
		References
Chapter-16---Semantic-similarity-based-descriptive-answer-e_2021_Web-Semanti
	16 Semantic similarity–based descriptive answer evaluation
		16.1 Introduction
		16.2 Literature survey
		16.3 Proposed system
			16.3.1 Wu and Palmer: word similarity
			16.3.2 Semantic similarity between a pair of sentences
			16.3.3 Semantic similarity between words (similarity matrix calculation)
		16.4 Algorithm
		16.5 Data set
		16.6 Results
		16.7 Conclusion and discussion
		References
Chapter-17---Classification-of-genetic-mutations-using-ontologi_2021_Web-Sem
	17 Classification of genetic mutations using ontologies from clinical documents and deep learning
		17.1 Introduction
		17.2 Clinical Natural Language Processing
		17.3 Clinical Natural Language Processing (Clinical NLP) techniques
			17.3.1 Statistical techniques in Clinical Natural Language Processing
				17.3.1.1 Bag of words
				17.3.1.2 Term frequency-inverse document frequency
				17.3.1.3 Rapid automatically keyword extraction
			17.3.2 Linguistic techniques in Clinical Natural Language Processing
				17.3.2.1 Part of speech tagging
				17.3.2.2 Tokenization
				17.3.2.3 Dependency graph
			17.3.3 Graphical techniques in Clinical Natural Language Processing
				17.3.3.1 TextRank
				17.3.3.2 Hyper link induced topic search
			17.3.4 Machine learning techniques in Clinical Natural Language Processing
				17.3.4.1 Support vector machine
				17.3.4.2 Word2Vec
			17.3.5 Deep learning techniques in Clinical Natural Language Processing
				17.3.5.1 Convolution neural network
				17.3.5.2 Recurrent neural network
		17.4 Clinical Natural Language Processing and Semantic Web
			17.4.1 Ontology creation from clinical documents
			17.4.2 Framework for classification of genetic mutations using ontologies from clinical document
		17.5 Case study: Classification of Genetic Mutation using Deep Learning and Clinical Natural Language Processing
		17.6 Conclusion
		References
Chapter-18---Security-issues-for-the-Semantic-Web_2021_Web-Semantics
	18 Security issues for the Semantic Web
		18.1 Introduction
			18.1.1 Security and cryptography
				18.1.1.1 Symmetric key cryptography or secret key cryptography
				18.1.1.2 Asymmetric key cryptography or public-key cryptography
			18.1.2 Introduction to Semantic Web
		18.2 Related work
		18.3 Security standards for the Semantic Web
			18.3.1 Securing the extensible markup language
			18.3.2 Securing the resource description framework
			18.3.3 Information interoperability in a secured way
				18.3.3.1 Management of trust for the Semantic Web
		18.4 Different attacks on the Semantic Web
			18.4.1 Importance of transport layer security on the Semantic Web
		18.5 Drawbacks of the existing privacy and security protocols in W3C social web standards
		18.6 Semantic attackers
		18.7 Privacy and Semantic Web
		18.8 Directions for future security protocols for the Semantic Web
		18.9 Conclusion
		References
Index_2021_Web-Semantics
	Index