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E-raamat: Intelligent Technologies for Web Applications

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  • Formaat: 367 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439871645
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Intelligent Technologies for Web ApplicationsSuurem pilt
  • Formaat: 367 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439871645
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The Internet has become an integral part of human life, yet the web still utilizes mundane interfaces to the physical world, which makes Internet operations somewhat mechanical, tedious, and less human-oriented. Filling a large void in the literature, Intelligent Technologies for Web Applications is one of the first books to focus on providing vital fundamental and advanced guidance in the area of Web intelligence for beginners and researchers.

The book covers techniques from diverse areas of research, including:















Natural language processing





Information extraction, retrieval, and filtering





Knowledge representation and management





Machine learning





Databases





Data, web, and text mining





Humancomputer interaction





Semantic web technologies











To develop effective and intelligent web applications and services, it is critical to discover useful knowledge through analyzing large amounts of content, hidden content structures, or usage patterns of web data resources. Intended to improve and reinforce problem-solving methods in this area, this book delves into the hybridization of artificial intelligence (AI) and web technologies to help simplify complex Web operations. It introduces readers to the state-of-the art development of web intelligence techniques and teaches how to apply these techniques to develop the next generation of intelligent Web applications.





The book lays out presented projects, case studies, and innovative ideas, which readers can explore independently as standalone research projects. This material facilitates experimentation with the books content by including fundamental tools, research directions, practice questions, and additional reading.
Preface xix
Authors xxv
Part I Introduction to the Web, machine learning, new AI techniques, and web intelligence
Chapter 1 Introduction to World Wide Web
3(18)
1.1 Brief history of the Web and the Internet
3(1)
1.2 Blogs
4(2)
1.3 Tweets
6(1)
1.4 Wikis
7(3)
1.4.1 Improving wiki content reliability, quality, and security
8(2)
1.5 Collaborative mapping
10(1)
1.6 Aggregation technologies
10(2)
1.7 Open platforms, application programming interface, and programming tools
12(1)
1.8 Web intelligence
13(2)
1.9 Intelligence in web applications
15(1)
1.10 Organization of this book
16(5)
Exercises
18(1)
References
19(2)
Chapter 2 Machine learning concepts
21(22)
2.1 Introduction
21(1)
2.2 Linear regression
22(1)
2.3 Supervised learning: Classification
23(5)
2.3.1 Evaluation measures
25(2)
2.3.2 Decision trees
27(1)
2.4 Support vector machines
28(1)
2.5 Nearest neighbor classifiers
29(1)
2.6 Unsupervised learning: clustering
29(4)
2.6.1 k-Means clustering
30(2)
2.6.2 Difference between clustering and nearest neighbor prediction
32(1)
2.6.3 Probabilistic clustering
33(1)
2.7 Hidden Markov models
33(1)
2.8 Bayesian methods
34(2)
2.8.1 Bayes theorem
34(1)
2.8.2 Naive Bayes
35(1)
2.8.3 Bayesian networks
36(1)
2.9 Reinforcement learning
36(1)
2.10 Applications of machine learning
37(3)
2.10.1 Speech recognition
37(1)
2.10.2 Computer vision
38(1)
2.10.3 Robotics
38(1)
2.10.4 Software engineering and programming language
39(1)
2.10.5 Computer games
39(1)
2.10.6 Machine learning for web
40(1)
2.11 Conclusion
40(3)
Exercises
41(1)
References
41(2)
Chapter 3 Overview of constituents for the new artificial intelligence
43(18)
3.1 Foundations of the new artificial intelligence and knowledge-based system
43(1)
3.1.1 Knowledge-based systems
43(1)
3.1.2 Limitations of symbolic systems
43(1)
3.2 Fuzzy systems
44(4)
3.2.1 Fuzzy set and fuzzy logic
44(1)
3.2.2 Fuzzy membership function
45(1)
3.2.3 Forms and operations of fuzzy functions
46(1)
3.2.4 Fuzzy relations and operations on fuzzy relations
46(1)
3.2.5 Fuzzy rule-based systems
47(1)
3.2.6 Applications of fuzzy logic
47(1)
3.3 Artificial neural networks
48(5)
3.3.1 Working of an artificial neuron
49(1)
3.3.2 Architectures of artificial neural network
50(1)
3.3.2.1 Multilayer perceptron architecture
50(2)
3.3.2.2 Kohonen architecture
52(1)
3.3.3 Applications of artificial neural network
52(1)
3.4 Genetic algorithms and evolutionary computing
53(4)
3.4.1 Basic principles of genetic algorithms
54(2)
3.4.2 An example of genetic algorithm to optimize a function
56(1)
3.4.3 Applications of genetic algorithms
56(1)
3.5 Rough sets
57(2)
3.5.1 Applications of rough sets
58(1)
3.6 Soft computing
59(1)
3.6.1 Applications of soft computing
59(1)
3.7 Benefits of the new AI to World Wide Web
60(1)
Exercises
60(1)
References
60(1)
Chapter 4 Web intelligence
61(22)
4.1 Internet, web, grid, and cloud
61(3)
4.1.1 Components of typical web
62(1)
4.1.2 Characteristics and benefits of the Web
63(1)
4.2 Introduction to web intelligence
64(6)
4.2.1 Semantic web
66(2)
4.2.2 Social intelligence
68(1)
4.2.3 Search engine techniques
68(1)
4.2.4 Web knowledge management
69(1)
4.2.5 Web information retrieval and filtering
69(1)
4.2.6 Web mining
69(1)
4.2.7 Web agents
69(1)
4.2.8 Human-computer integration
70(1)
4.3 Perspectives of WI
70(2)
4.4 Levels of WI
72(1)
4.4.1 Imparting intelligence at basic infrastructural level
72(1)
4.4.2 Imparting intelligence at knowledge level
72(1)
4.4.3 Imparting intelligence at interface level
72(1)
4.4.4 Imparting intelligence at application level
73(1)
4.5 Goal of WI
73(1)
4.6 Characteristics of web intelligence
73(2)
4.6.1 Openness
73(1)
4.6.2 Intelligent
73(1)
4.6.3 Secured
74(1)
4.6.4 User friendly
74(1)
4.6.5 Agent based
74(1)
4.6.6 Interoperability
74(1)
4.6.7 Global knowledge base
74(1)
4.7 Challenges and issues of WI
75(1)
4.7.1 Nature of knowledge
75(1)
4.7.2 Volume, complexity, and unstructured environment of web
75(1)
4.7.3 Development methods, protocols, security, and quality standards
75(1)
4.7.4 Weak support from AI
75(1)
4.8 Wisdom web
75(1)
4.8.1 Autonomic regulation of functionalities between the resources
76(1)
4.8.2 Embedding knowledge into the Web
76(1)
4.8.3 Improving access mechanisms
76(1)
4.9 Web-based support systems
76(1)
4.10 Designing an intelligent web
77(1)
4.11 Future of WI
77(6)
Exercises
78(1)
References
78(5)
Part II Information retrieval, mining, and extraction of content from the Web
Chapter 5 Web information retrieval
83(28)
5.1 Introduction
83(2)
5.1.1 Managing web data
83(1)
5.1.2 Context and web IR
84(1)
5.2 Typical web search engines
85(4)
5.2.1 Introduction to web crawler
86(1)
5.2.2 Some early work in the area of web crawlers
87(2)
5.2.3 Google searching
89(1)
5.3 Architecture of a web crawler
89(3)
5.4 Distributed crawling
92(1)
5.5 Focused spiders/crawlers
92(3)
5.5.1 Architecture of the focused crawler
94(1)
5.5.2 Operational phases for the focused crawler
94(1)
5.5.3 Measuring relevance of the focused crawlers
95(1)
5.6 Collaborative crawling
95(1)
5.7 Some tools and open source for web crawling
96(1)
5.8 Information retrieval: beyond searching
97(2)
5.9 Models of information retrieval
99(1)
5.9.1 Boolean model and its variations
99(1)
5.9.2 Vector space model
99(1)
5.9.3 Probabilistic models
100(1)
5.9.4 Latent semantic indexing
100(1)
5.10 Performance measures in IR
100(1)
5.11 Natural language processing in conjunction with IR
101(2)
5.11.1 Generic NLP architecture of IR
102(1)
5.12 Knowledge-based system for information retrieval
103(3)
5.13 Research trends
106(1)
5.13.1 Semantic information
106(1)
5.13.2 Multimedia data
107(1)
5.13.3 Opinion retrieval
107(1)
5.14 Conclusion
107(4)
Exercises
107(1)
References
108(3)
Chapter 6 Web mining
111(22)
6.1 Introduction to web mining
111(1)
6.1.1 Web as a graph
112(1)
6.2 Evolution of web mining techniques
112(1)
6.3 Process of web mining
113(2)
6.4 Web content mining
115(6)
6.4.1 Classification
115(1)
6.4.2 Cluster analysis
116(1)
6.4.3 Association mining
117(1)
6.4.4 Structured data extraction
118(1)
6.4.5 Unstructured content extraction
118(2)
6.4.6 Template matching
120(1)
6.5 Web usage mining
121(2)
6.5.1 Activities in web usage mining
121(1)
6.5.2 Retrieval sources for web usage mining
121(1)
6.5.3 Cleaning and data abstraction
122(1)
6.5.4 Identification of required information
122(1)
6.5.5 Pattern discovery and analysis
123(1)
6.6 Web structure mining
123(2)
6.6.1 HITS concept
123(1)
6.6.2 PageRank method
124(1)
6.7 Sensor web mining: architecture and applications
125(2)
6.8 Web mining software
127(1)
6.9 Opinion mining
127(2)
6.9.1 Feature-based opinion mining
129(1)
6.10 Other applications using AI for web mining
129(1)
6.11 Future research directions
130(3)
Exercises
131(1)
References
131(2)
Chapter 7 Structured data extraction
133(40)
7.1 Preliminaries
133(7)
7.1.1 Structured data
133(2)
7.1.2 Information extraction
135(2)
7.1.3 Evaluation metrics
137(1)
7.1.4 Approaches to information extraction
138(1)
7.1.5 Free, structured, and semistructured text
138(1)
7.1.6 Web documents
139(1)
7.2 Wrapper induction
140(12)
7.2.1 Wrappers
140(1)
7.2.2 From information extraction to wrapper generation
140(1)
7.2.3 Wrapper generation
141(1)
7.2.3.1 Semiautomated wrapper generation
142(3)
7.2.3.2 Automated wrapper generation
145(6)
7.2.4 Inductive learning of wrappers
151(1)
7.3 Locating data-rich pages
152(1)
7.3.1 Finding tables
152(1)
7.3.2 Identifying similarities
152(1)
7.3.3 Heuristics on product properties
153(1)
7.3.4 Human intrusion
153(1)
7.4 Systems for wrapper generation
153(10)
7.4.1 Structured and semistructured web pages
154(1)
7.4.1.1 ShopBot
154(1)
7.4.1.2 Wrapper induction environment
154(1)
7.4.1.3 SoftMealy
155(1)
7.4.1.4 Supervised learning algorithm for inducing extraction rules
156(2)
7.4.1.5 WebMantic
158(1)
7.4.2 Semistructured and unstructured web pages
159(1)
7.4.2.1 Robust automated production of IE rules
160(1)
7.4.2.2 Sequence rules with validation
161(1)
7.4.2.3 WHISK
162(1)
7.5 Applications and commercial systems
163(3)
7.5.1 Examples of applications
164(1)
7.5.2 Commercial systems
164(1)
7.5.2.1 Junglee
165(1)
7.5.2.2 Jango
165(1)
7.5.2.3 MySimon
166(1)
7.6 Summary
166(7)
Exercises
167(1)
References
167(6)
Part III Semantic web and web knowledge management
Chapter 8 Semantic web
173(40)
8.1 Introduction to semantic web
173(1)
8.2 Metadata
174(6)
8.2.1 Dublin core metadata standard
176(1)
8.2.2 Metadata objectives
176(1)
8.2.2.1 Simplicity of creation and maintenance
176(3)
8.2.2.2 Commonly understood semantics
179(1)
8.2.2.3 International scope
179(1)
8.2.2.4 Extensibility
179(1)
8.2.2.5 Interoperability
179(1)
8.3 Layered architecture of semantic web
180(1)
8.3.1 Unicode and uniform resource identifier
180(1)
8.3.2 Extensible markup language
180(1)
8.3.3 Resource description framework
180(1)
8.3.4 RDF schema
181(1)
8.3.5 Ontology
181(1)
8.3.6 Logic and proof
181(1)
8.3.7 Trust
181(1)
8.4 Refined architecture of semantic web
181(1)
8.5 Ontology and ontology constructs
182(16)
8.5.1 Extensible markup language
184(3)
8.5.2 Resource description framework
187(1)
8.5.3 Web ontology language
188(2)
8.5.3.1 OWL full
190(1)
8.5.3.2 OWL DL
190(1)
8.5.3.3 OWL lite
190(1)
8.5.4 Ontology interchange language
191(1)
8.5.5 OWL2 profile
191(3)
8.5.6 SPARQL
194(2)
8.5.6.1 Result syntaxes
196(1)
8.5.6.2 Query for relationships
196(1)
8.5.6.3 Transform data with CONSTRUCT
196(1)
8.5.6.4 OPTIONAL
196(1)
8.5.6.5 Negation
197(1)
8.6 Meta-ontology
198(1)
8.7 Ontology tools and editors
198(1)
8.8 Annotation tools
199(1)
8.9 Inference engines
199(1)
8.10 Semantic web applications
200(7)
8.10.1 Search engine
200(1)
8.10.2 Semantic web portals
201(1)
8.10.3 Catalog management and thesaurus
201(1)
8.10.4 Call center
201(1)
8.10.5 e-Learning
201(1)
8.10.6 Tourism
202(2)
8.10.7 Publishing
204(1)
8.10.8 Community and social projects
204(1)
8.10.9 e-Commerce
205(1)
8.10.10 Health care
205(1)
8.10.11 Digital heritage
205(1)
8.10.12 Open archives
206(1)
8.11 Semantic web interoperability and web mining
207(1)
8.12 Semantic web and social communities
207(1)
8.13 Semantic web and intelligent search
208(2)
8.14 Semantic web research issues
210(3)
Exercises
210(1)
References
211(2)
Chapter 9 Web knowledge management
213(26)
9.1 About knowledge
213(1)
9.2 Knowledge management fundamentals
213(4)
9.2.1 Architecture of the knowledge management process
215(1)
9.2.2 Benefits of knowledge management
216(1)
9.2.3 Challenges of knowledge management
217(1)
9.3 Ontology revisited
217(5)
9.3.1 Ontology examples
217(2)
9.3.2 Ontology classification
219(1)
9.3.3 Parameters to build ontology
220(1)
9.3.4 Standards and interoperability for ontology
221(1)
9.3.5 Ontology on the Web
221(1)
9.4 Utilization of knowledge management methodologies on semantic web
222(6)
9.4.1 Literature review
223(1)
9.4.2 General architecture for web knowledge management
224(1)
9.4.3 Semantically enhanced knowledge management
225(1)
9.4.3.1 Semantic wiki
226(1)
9.4.3.2 Semantic annotation tools
227(1)
9.4.4 Issues and challenges
228(1)
9.5 Exchanging knowledge in virtual entities
228(5)
9.5.1 Virtual world
228(1)
9.5.2 Virtual organizations
229(1)
9.5.3 Knowledge management and intelligent techniques within virtual entities
230(2)
9.5.4 Virtual communities and semantic web
232(1)
9.6 Case study
233(1)
9.7 Building the World Wide Why
234(1)
9.8 Conclusion and applications
234(5)
Exercises
235(1)
References
235(4)
Chapter 10 Social network intelligence
239(24)
10.1 Introduction to social networking
239(4)
10.1.1 Web patterns and social ecosystem
242(1)
10.1.2 Types of social networks
242(1)
10.2 Friend-of-a-friend
243(5)
10.3 Semantically interlinked online communities
248(1)
10.4 Social network analysis
249(2)
10.5 Social network data
251(1)
10.6 hCard and XFN
252(2)
10.7 Advantages and disadvantages of social networking
254(1)
10.7.1 Advantages of social networking
254(1)
10.8 Social graph application programming interface
254(1)
10.9 Social search and artificial intelligence
255(2)
10.9.1 Intelligent social networks
257(1)
10.10 Research future
257(6)
Exercises
258(1)
References
258(5)
Part IV Agent-based web, security issues, and human-computer interaction
Chapter 11 Agent-based web
263(30)
11.1 Introduction
263(1)
11.2 Agents
264(3)
11.2.1 Characteristics and advantages
264(2)
11.2.2 Agents and objects
266(1)
11.2.3 Agents and web services
266(1)
11.3 Typology of agents
267(5)
11.3.1 Collaborative agent
267(2)
11.3.2 Interface agent
269(1)
11.3.3 Mobile agent
269(1)
11.3.4 Information agent
269(1)
11.3.5 Intelligent agent
270(2)
11.3.6 Hybrid agent
272(1)
11.4 Multiagent systems
272(2)
11.4.1 Multiagent system framework
273(1)
11.4.2 Communication between agents
274(1)
11.5 Agent-based web
274(7)
11.5.1 Generic architecture of agent-based web
275(2)
11.5.2 Example agents
277(1)
11.5.2.1 Agent for query and information retrieval
277(1)
11.5.2.2 Filtering agent
277(1)
11.5.2.3 Interface agent
278(1)
11.5.2.4 Personal assistance agent
278(1)
11.5.2.5 e-Commerce agent
279(1)
11.5.2.6 e-Communities and agents
279(1)
11.5.2.7 Ontology management
280(1)
11.6 Hybridization of mobile agent and interface agent: A case for personalized content representation
281(5)
11.6.1 Mobile agents: Characteristics and working
281(1)
11.6.2 Hybridization of a mobile agent with an interface agent
282(2)
11.6.3 Personalized content representation through the hybrid agent
284(2)
11.7 Case study
286(5)
11.7.1 Multiagent system for oil company
286(1)
11.7.2 RETSINA calendar agent
287(2)
11.7.2.1 OpenStudy.com
289(1)
11.7.2.2 Cobot
290(1)
11.8 Conclusion
291(2)
Exercises
291(1)
References
292(1)
Chapter 12 Web security
293(14)
12.1 Introduction
293(1)
12.2 Web vulnerabilities
294(5)
12.2.1 Scripting languages
294(1)
12.2.2 Understanding communication
295(1)
12.2.3 Injection flaws
296(1)
12.2.4 Cross-site scripting
297(1)
12.2.5 Cross-site request forgery
298(1)
12.2.6 Phishing attacks
298(1)
12.2.7 Information leakage
299(1)
12.2.8 Browsers compromising privacy
299(1)
12.3 Web server protection
299(2)
12.3.1 Firewall
299(1)
12.3.2 Intrusion detection system
300(1)
12.4 Security and privacy
301(1)
12.5 Contributions of AI for security issues
302(5)
Exercises
304(1)
References
305(2)
Chapter 13 Human-web interactions
307(22)
13.1 Introduction
307(1)
13.2 Features of a good website
308(1)
13.2.1 Content
308(1)
13.2.2 Information organization
308(1)
13.2.3 Performance
309(1)
13.2.4 Compatibility
309(1)
13.2.5 Visual design
309(1)
13.2.6 Interaction design
309(1)
13.3 What is interaction?
309(2)
13.3.1 Common interaction styles
310(1)
13.3.2 Three-dimensional interactions
310(1)
13.4 Interaction design and related parameters
311(2)
13.5 Usability
313(1)
13.5.1 World usability day
314(1)
13.6 Process of interaction design
314(1)
13.6.1 Know your users, their requirements, and identify the objective of the system
315(1)
13.6.2 Check the feasibility and cost-benefit ratio of different alternatives
315(1)
13.6.3 Build selected alternatives considering content
315(1)
13.6.4 Test the developed interaction design
315(1)
13.6.5 Conduct postimplementation review and update if required
315(1)
13.7 Conceptual models of interaction
315(1)
13.8 Interface
316(1)
13.9 Interface design methods
317(4)
13.9.1 Activity-centered design
317(1)
13.9.2 Body storming
317(1)
13.9.3 Contextual design
318(1)
13.9.4 Focus group
318(1)
13.9.5 Iterative design
318(1)
13.9.6 Participatory design
319(1)
13.9.7 Task analysis
319(1)
13.9.8 User-centered design
319(1)
13.9.9 Usage-centered design
320(1)
13.9.10 User scenario
320(1)
13.9.11 Value-sensitive design
320(1)
13.9.12 Wizard of Oz experiment
320(1)
13.10 Tools for human-web interaction
321(1)
13.11 Interaction evaluation methods
321(1)
13.11.1 Cognitive walk-through
321(1)
13.11.2 Heuristic evaluation
321(1)
13.11.3 Review-based evaluation
322(1)
13.11.4 Evaluating through user participation
322(1)
13.11.5 Evaluating implementations
322(1)
13.12 Human-computer interaction and human-web interaction
322(1)
13.13 Issues in human-web interactions
323(1)
13.14 Support of AI for human-web interactions
323(4)
13.14.1 Searching, retrieval and filtering, and semantic search
324(1)
13.14.2 Native language interface and fuzzy logic for web applications
325(1)
13.14.3 Knowledge management and knowledge representation on the Web
325(1)
13.14.4 Agent-based systems
326(1)
13.14.5 Modeling users experience and usability for better web interactions
326(1)
13.14.6 Intelligent web mining
326(1)
13.14.7 Interacting with smart environments
326(1)
13.15 Case studies
327(1)
13.15.1 MIT intelligent room
327(1)
13.15.2 MediaBlocks system
327(1)
13.15.3 PhotoHelix
328(1)
13.16 Research applications
328(1)
13.17 Conclusion
329(1)
Exercises 329(1)
References 330(1)
Index 331
Priti Srinivas Sajja joined the faculty of the Department of Computer Science, Sardar Patel University, India in 1994 and presently works as an Associate Professor. She received her M.S. (1993) and Ph.D (2000) in Computer Science from the Sardar Patel University. Her research interests include knowledge-based systems, soft computing, multiagent systems, and software engineering. She has more than 100 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Four of her publications have won best research paper awards. She is co-author of 'Knowledge-Based Systems'. She is supervising work of seven doctoral research students. She is Principal Investigator of a major research project funded by UGC, India. She is serving as a member in editorial board of many international science journals and served as program committee member for various international conferences.





Rajendra Akerkar is a professor/senior researcher at the Western Norway Research Institute (Vestlandsforsking), Norway. His research and teaching experience includes over 20 years in the Academia spanning different universities in Asia, Europe and North America. As founder, he is instrumental in ensuring that the Technomathematics Research Foundation (TMRF) lends a platform for research in India. Under his leadership, TMRF has become one of the well-known organizations amongst the research community worldwide. His current research agenda basically concentrates on learning and language - how each works in the human and how they can be replicated in a machine. He received DAAD fellowship in 1990 and also awarded prestigious BOYSCASTS Young Scientist award of Department of Science & Technology, Government of India, in 1997. He is an Editor-in-Chief of International Journal of Computer Science & Applications, and Associate Editor of International Journal of Metadata, Ontologies and Semantics. He is
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