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Intelligent Applications for Web Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info.

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Presentation on theme: "Intelligent Applications for Web Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info."— Presentation transcript:

1 Intelligent Applications for Web Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info for detail 1Created By Priti Srinivas Sajja

2 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 2 Created By Priti Srinivas Sajja Name: Dr. Priti Srinivas Sajja Communication: Mobile : URL : Academic qualifications : Ph. D in Computer Science Thesis title: Knowledge-Based Systems for Socio- Economic Development (2000) Subject area of specialization : Artificial Intelligence Publications : 108 in Books, Book Chapters, Journals and in Proceedings of International and National Conferences

3 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 3 Created By Priti Srinivas Sajja Introduction Natural Intelligence Responds to situations flexibly. Makes sense of ambiguous or erroneous messages. Assigns relative importance to elements of a situation. Finds similarities even though the situations might be different. Draws distinctions between situations even though there may be many similarities between them. Artificial Intelligence According to Rich & Knight (1991) “AI is the study of how to make computers do things, at which, at the moment, people are better”. A machine is regarded as intelligent if it exhibits human characteristics generated through natural intelligence. AI is the study of human thought processes and moving toward problem solving in a symbolic and non-algorithmic way.

4 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 4 Created By Priti Srinivas Sajja Introduction “Artificial Intelligence(AI) is the study of how to make computers do things at which, at the moment, people are better ” Elaine Rich, Artificial Intelligence, McGraw Hill Publications, 1986

5 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 5 Created By Priti Srinivas Sajja Introduction Extreme solution, either best or worst taking  (infinite) time time Acceptable solution in acceptable time Nature of AI solutions where people are better human thought process characteristics we associate with intelligence knowledge using symbols heuristic methods non-algorithmic Constituents of artificial intelligence

6 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 6 Created By Priti Srinivas Sajja Testing Intelligence AI Tests Turing test will fail to test for intelligence in two circumstances; 1.A machine may well be intelligent without being able to chat exactly like a human; and; 2.The test fails to capture the general properties of intelligence, such as the ability to solve difficult problems or come up with original insights. If a machine can solve a difficult problem that no person could solve, it would, in principle, fail the test. Can you tell me what is *67344 ? Why Sir? The Boss could not judge who was replying, thus the machine is as intelligent as the secretary. The Turing test

7 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 7 Created By Priti Srinivas Sajja Can you find any test to check the given system is intelligent or not? AI Tests If it talks like human Translates, summarizes, and learns Solves your problem Reacts differently Walks, perceives, tests, smells, and feels like human Makes and understands joke

8 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 8 Created By Priti Srinivas Sajja Rich & Knight (1991) classified and described the different areas that Artificial Intelligence techniques have been applied to as follows: Applications Mundane Tasks Perception - vision and speech Natural language understanding, generation, and translation Commonsense reasoning Robot control Formal Tasks Games - chess, backgammon, checkers, etc. Mathematics- geometry, logic, integral calculus, theorem proving, etc. Expert Tasks Engineering - design, fault finding, manufacturing planning, etc. Scientific analysis Medical diagnosis Financial analysis

9 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 9 Created By Priti Srinivas Sajja Basic transactions by operational staff using data processing Middle management uses reports/info. generated though analysis and acts accordingly Higher management generates knowledge by synthesizing information Strategy makers apply morals, principles, and experience to generate policies Wisdom (experience) Knowledge (synthesis) Information (analysis) Data (processing of raw observations ) VolumeSophistication and complexity TPS DSS, MIS KBS WBS IS Data pyramid Data Pyramid

10 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 10 Created By Priti Srinivas Sajja According to the classifications by Tuthhill & Levy (1991), five main types of KBS exists:  Expert systems  Linked Systems  CASE based Systems  Intelligent Tutoring Systems  Intelligent User Interface for Database Knowledge base Inference engine User interface Explanation and reasoning Explanation and reasoning Self- learning Self- learning General structure of KBS Knowledge Based systems Knowledge Based systems

11 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 11 Created By Priti Srinivas Sajja Knowledge Based systems Knowledge Based systems Experience Satellite Broadcasting (Internet, TV, and Radio) Printed Media Experts Sources of knowledge Types of Knowledge Tacit knowledge Explicit knowledge Commonsense knowledge Informed commonsense knowledge Heuristic knowledge Domain knowledge Meta knowledge Types of Knowledge Tacit knowledge Explicit knowledge Commonsense knowledge Informed commonsense knowledge Heuristic knowledge Domain knowledge Meta knowledge

12 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence  Intelligence, explanation and reasoning  Partial self learning, uncertainty handling  Documentation of knowledge  Proactive problem solving  Cost effectiveness  Nature of knowledge  Large volume of knowledge  Knowledge acquisition techniques  Little support to engineer AI based systems  Shelf life of knowledge and system  Development Effort 12 Created By Priti Srinivas Sajja Pros and Cons

13 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Bio-Inspired Computing  New approaches to AI  Taking inspiration form nature and biological systems  Includes models such as  Artificial Neural Network (ANN),  Genetic Algorithm(GA),  Swarm Intelligence(SI), etc.  Nature has virtues of self learning, evolution, emergence and immunity  The objective of bio-inspired models and techniques to take inspiration from Mother Nature and solve problems in more effective and intelligent way 13 Created By Priti Srinivas Sajja Bio-inspired

14 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Artificial Neural Network (ANN)  An artificial neural network (ANN) is connectionist model of programming using computers.  An ANN attempts to give computers humanlike abilities by mimicking the human brain’s functionality.  The human brain consists of a network of more than a hundred billions interconnected neurons working in a parallel fashion. 14 Created By Priti Srinivas Sajja A biological neuron An artificial neuron X2X2 X1X1 XiWiXiWi W1W1 W2W2 … …. y XnXn WnWn Bio-inspired

15 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence A Perceptron Multilayer Neural Network 15 Created By Priti Srinivas Sajja W 12 X1X2X3....XnX1X2X3....Xn O 0 O 1 …. O m W 1h Input layerHidden layers Output layer Bio-inspired

16 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Genetic Algorithms (GA) It mimics Nature’s evolutionary approach The algorithm is based on the process of natural selection— Charles Darwin’s “survival of the fittest.” GAs can be used in problem solving, function optimizing, machine learning, and in innovative systems. 16 Created By Priti Srinivas Sajja Genetic cycle Modify with operations Modify with operations Start with initial population by randomly selected Individuals Start with initial population by randomly selected Individuals Evaluate fitness of new individuals Evaluate fitness of new individuals Update population with better individuals and repeat Update population with better individuals and repeat Initial population Selection Mutation Crossover Evaluating new individuals through fitness function Modify the population Bio-inspired

17 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Swarm Intelligence  Inspired by the collective behavior of social insect colonies and other animal societies  Ant colony, fish school, bird flocking and honey comb are the examples 17 Created By Priti Srinivas Sajja Bio-inspired

18 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Some more examples …. 18 Created By Priti Srinivas Sajja Bio-inspired

19 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 19 Created By Priti Srinivas Sajja Web

20 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 20 Created By Priti Srinivas Sajja Web Internet can be defined as network of networks. The World Wide Web (WWW or Web) is a large scale distributed hypermedia system on the internet platform. The WWW is based on the HTTP-protocol for data transfer, HTML markup for content display on top of the Internet infrastructure that uses different protocols and content description schemes. According to Hans-Georg Stork (2002), the Web is experiencing two issues: Not able for “semantic” access and use problem Depends on the universality of physical access via high-bandwidth local loops and broadband wireless channels.

21 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 21 Created By Priti Srinivas Sajja Web Semantic Web is an extension of the current Web in which information is given well defined meaning by associating metadata. (Berners-Lee, Hendler, & Lassila, 2001). Basic objective of a semantic web is “ Making content machine-understandable ”. The semantic web aims to allow Web entities (software agents, users, and programs) for interoperating, dynamically discovering and using resources, extracting knowledge, and solving complex problems.

22 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 22 Created By Priti Srinivas Sajja Web Intelligence Web Intelligence Challenges and limitations of the current Web  Lack of knowledge-based searches  Lack of effective techniques to access the Web in depth  Lack of mechanisms to deal with dynamic requirements of users  Lack of automatically constructed directories  Lack of multi-dimensional analysis and data mining support By employing the AI techniques for web functions, it is possible to partly impart intelligence in web-based business. The Web Intelligence (WI) is considered as employment of AI techniques for the Web. Web Technology Platform of Internet Protocols and standards Browser Search engine Semantic Web Other software Web Technology Platform of Internet Protocols and standards Browser Search engine Semantic Web Other software AI Techniques Knowledge representation Knowledge management Expert system Heuristic functions New AI methods AI Techniques Knowledge representation Knowledge management Expert system Heuristic functions New AI methods Web Intelligence

23 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 23 Created By Priti Srinivas Sajja Web Intelligence Web Intelligence Web Information Retrieval  Information retrieval and filtering  Performance measures  NLP Web Information Retrieval  Information retrieval and filtering  Performance measures  NLP Web Mining  Web log mining  Web structure mining  Web content mining  Sensor Web mining Web Mining  Web log mining  Web structure mining  Web content mining  Sensor Web mining Web Agents  Intelligent agents  Multi agent systems  Pattern discovery Web Agents  Intelligent agents  Multi agent systems  Pattern discovery Human Computer Interaction/NLP  Personalized interface  Multi lingual interfaces  Usability Human Computer Interaction/NLP  Personalized interface  Multi lingual interfaces  Usability Semantic Web  Search Engine  Ontology management  Meta ontology  Interoperability  Inference Semantic Web  Search Engine  Ontology management  Meta ontology  Interoperability  Inference Social Intelligence  Popular tools and techniques  Social Network Analysis Social Intelligence  Popular tools and techniques  Social Network Analysis Search Engine Techniques  Customized searches  Meta search engine  Search engine optimization Search Engine Techniques  Customized searches  Meta search engine  Search engine optimization Web Knowledge Management  Knowledge management architecture for Web  Security Web Knowledge Management  Knowledge management architecture for Web  Security

24 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 24 Created By Priti Srinivas Sajja To implement a simple Web crawler following steps can be performed. 1.Start interaction with user and seek keywords and URL to start with 2.Add the URL to list to search for 3.Repeat while list is not empty 3.1Consider the first URL and mark with appropriate flag 3.2If the protocol of the selected URL is not HTTP then break 3.3Follow the robot.txt file (instructions), if any 3.4Open the URL 3.5If the URL is not an HTML file then break else add the file into list of files found 3.6 Extract links by traversing the file 3.7 Repeat this procedure for every link within the file Searching

25 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 25 Created By Priti Srinivas Sajja Searching Web crawler process Spider Lists Index Processing Storage Web Focused Crawler Searching relevant pages Simple Crawler Searching all pages Scope of focused crawler

26 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 26 Created By Priti Srinivas Sajja Information Retrieval (IR) is a science of information finding, acquiring, storing and utilizing the information for problem solving. The formal steps are given as follows: Indexing Query formulation Matching query representation Relevance feedback and interactive retrieval Information Retrieval Information Retrieval

27 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 27 Created By Priti Srinivas Sajja Information Retrieval Information Retrieval Models of Information Retrieval 1.Boolean Model - Boolean operators like AND, OR and NOT are applied to retrieve content. 2.Vector space model - represents the documents and queries as vectors (defined by keywords) in a space having more than one dimensions. 3.Probabilistic model - considers the retrieved content according to some rank based on some probability. 4.Latent semantic model - considers associations among terms and documents to retrieve required content.

28 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 28 Created By Priti Srinivas Sajja NLP for IR Generic NLP architecture User Web Interpreted Query GrammarLexiconToken Templates Terminology TokenizerRecognizerParserPreprocessing Interpreter Natural Query Search Result Web Search Request Filtered Result Dialog Processor Search Mechanism Search Result Dialog Analyzer and Generator Context Model and Domain Terminology User Profile and Local information

29 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 29 Created By Priti Srinivas Sajja Research Trend in IR Research Trend in IR Research Trend in IR Heuristic filtering Semantic Information Multimedia Data Opinion Retrieval Information retrieval and translation Fuzzy Boolean model of information retrieval

30 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 30 Created By Priti Srinivas Sajja Knowledge Management Knowledge Management Discover Use Document Share Knowledge Base Knowledge Sources Knowledge Engineer Organizational Requirements Standards, Protocols, and Services Typical Knowledge Management Process The Web follows document-centric approach, which lacks efficient representation and access of the content on Web.

31 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 31 Created By Priti Srinivas Sajja Knowledge Management Knowledge Management Knowledge Management Architecture on the Web Local Documents Domain Ontology Knowledge repository Editor Crawler Inference mechanism Metadata Knowledge Presentation User Profiles Knowledge Processing Knowledge Discovery Experts Administrator Web Users Service Standards

32 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 32 Created By Priti Srinivas Sajja Knowledge Management on Web Autonomous agents for knowledge discovery Protocols for knowledge share and use Ontology editors K-Commerce Knowledge management models Virtual world Wisdom Web Knowledge Management Knowledge Management

33 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 33 Created By Priti Srinivas Sajja Web Mining Data Mining  The data mining techniques are dedicated techniques that extract patterns and useful information from the existing known sources of data. Text Mining  Text mining techniques are used to find, organize and discover information from the textual resources. Web Mining  Web mining techniques are used to find, organize and discover information from the huge unstructured platform such as Web.

34 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 34 Created By Priti Srinivas Sajja Web Mining Challenges of Web Mining  Structure highly unstructured  Size tremendous  Nature dynamic  Accessibility global by anybody  Redundant similar information in many formats  Noise virus, malware and adware

35 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 35 Created By Priti Srinivas Sajja Web Mining Web Content Mining Web Log Mining Web Structure Mining Data Type /Sources Purpose Any data Textual data Web data Finding relevant data Finding pattern and knowledge Data Retrieval Information Retrieval Web Retrieval Data Mining Text Mining Web Mining Web Mining and Other Related Activities

36 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Web Content Mining  It attempts to mine content of the Web to discover useful patterns through hyperlinks.  The content may be text, images, audio, video, and structured data like tables and graphs.  The web content mining goes beyond keyword extraction and requires advanced techniques such as NLP and AI.  Web content mining strategies are of two groups  one that directly mine the content of documents and  second that improves on the content search of other tools like search engines. 36 Created By Priti Srinivas Sajja Web Mining

37 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Classification as Web Content Mining Techniques  Classification : deals with classification of the content into various groups as accurate as possible. The training sets and test (validation) sets are provided to the classification algorithm to build and to test the classification model respectively. Typical classification techniques include:  Decision tree based methods;  Rule base classification;  Supervised learning through artificial neural network;  Evolutionary techniques; and  Support vector machines; 37 Created By Priti Srinivas Sajja Web Mining

38 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Clustering as Web Content Mining Techniques  Clustering : deals with finding groups of similar objects based on the content characteristics itself in unsupervised approach. 38 Created By Priti Srinivas Sajja Web Mining Partition and hierarchical clustering Initial Points Partition Clustering 1, 2 and 3 are independent clusters Hierarchical Clustering Here cluster 3 is subset of 2; and 2 is subset of

39 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Classification and Clustering as Web Content Mining Techniques  Association Mining : deals with discovering interesting relations between variables in large databases. This technique find rules that will predict the occurrence of an entity based on general pattern exists in the given data sets.  Consider following example. 39 Created By Priti Srinivas Sajja Web Mining

40 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Classification as Web Content Mining Techniques  Opinion Mining : deals with extraction of opinion of users learn attitude of the content, person or product. Opinion mining plays an important role in mining applications for customer relationship management, consumer attitude detection, brand and product positioning, product reviews, and market research.  Feature based opinion mining mines the Web content by given features of a specified product/entity.  Once the opinions are collected, they are further grouped and analyzed. 40 Created By Priti Srinivas Sajja Web Mining

41 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Some other Web Content Mining Techniques  Structured data extraction : Structured data extraction deals with extraction of important information about product, services and data records that are available in structured form on host pages.  Unstructured content extraction: It deals with extraction of content that is not available in structured form.  Web information integration: It extracts content form multiple site, checks for redundancy, and integrates information. Vice versa, the content mining can be used for web site classification/clustering also.  Detecting noise: The malware, adware and virus from multiple site can be identified and blocked.  Opinion mining: The customer surveys, opinion, sentiments and product review information etc can be extracted here. 41 Created By Priti Srinivas Sajja Web Mining

42 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Web Usage Mining  The Web usage mining provides the collection of information accessed so far to its users.  Web usage mining highlights the behavior of users on the Web and understands access patterns and trends.  The web usage mining deals with web log and accumulated data on web servers in order to understand the user behavior and the web structure.  There are two main purposes for web usage mining. The first one is to track general access pattern and second is customized usage tracking. 42 Created By Priti Srinivas Sajja Web Mining

43 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 43 Created By Priti Srinivas Sajja Web Mining Activities for web usage mining Log Data Registration Data Other Information Analysis and Use Integration & Analysis of Patterns Discovery Cleaning Noise Cleaning Malware Pattern discovery and Analysis Retrieval Cleaning Identification Integration Use

44 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Web Structure Mining  The Web behaves like a hypertext document information system. The Web objects such as pages and sites are generally exist between the numbers of links.  Web structure mining focuses with structure of such hyperlinks on the Web.  There are two basic techniques to analyze the network of links on the Web. These methods are (i)Hyperlinked Induced Topic Search ( HITS ) concept and (ii)Page Rank method.  The Web may be represented as a huge directed graph structure. 44 Created By Priti Srinivas Sajja Web Mining

45 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Sensor Web Mining  Data are collected from different sensors placed at remote places.  Provides opportunity of efficient geo-referencing in remote fashion. 45 Created By Priti Srinivas Sajja Web Mining Solar panel Microcontroller Radio Memory Sensor Suit Architecture of a Pod  Sensor Web consists number of sensor platforms called pods.  Each pod senses some dynamic environmental data in real time fashion.  Radio is used to connect the pod with its local neighborhood.  Applications are weather forecasting, costal area monitoring, communication and education, and eco-system information and manageme nt.

46 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 46 Created By Priti Srinivas Sajja Web Mining

47 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence AI for Web Mining  Mining pro-active agents  ANN for finding/ analyzing patterns  Fuzzy partitions and clustering  Evolution of patterns from Web  Heuristic based filtering functions for mining  Sentiment mining using NLP on social network platform 47 Created By Priti Srinivas Sajja Web Mining

48 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence Types of Agents  Collaborative Agent  Interface Agent  Mobile Agent  Information Agent  Intelligent Agent  Hybrid Agent 48 Created By Priti Srinivas Sajja Agent Sensors to acquire environmental information and user’s requirement Action interface Autonomy Cooperation Learning Mobility Proactivity An Agent Agent Based Web Agent Based Web

49 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 49 Created By Priti Srinivas Sajja Example Web/ Semantic Web Internet Browsers Filtering Agent Interface Agent URL Management Ontology Agent Search Engine Agent Social Networking Agent Agent based web Core Services Customized Services Ontology Tools Query Agent Document Management Agent Protocols and Standards Agent Based Web Agent Based Web

50 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 50 Created By Priti Srinivas Sajja Web Client Web Bowser Knowledge Base Ontology Local Databases BaseDomain Databases Base Query ManagerSearch EngineVisualization Figure Information retrieval agent Agent Based Web Agent Based Web Example

51 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence  Agent for semantic analysis  Verification and validation (V&V) agent  Finding suitable web services agent  Crawler agent  Explanation and reasoning Agent  Natural language interface agent  Communication agent  Network traffic management agent  Mobile agent for personalized content representation 51 Created By Priti Srinivas Sajja Agent Based Web Agent Based Web Other Examples Other Examples

52 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 52 Created By Priti Srinivas Sajja Major References  Knowledge-based systems, Akerkar RA and Priti Srinivas Sajja, Jones & Bartlett Publishers, Sudbury, MA, USA (2009) Knowledge-based systems  Intelligent technologies for web applications”, Priti Srinivas Sajja, Rajendra Akerkar; CRC Press (Taylor & Francis Group), Boka Raton, FL, USA (2012) Other References  llustrationsOf.com  coders-view.blogspot.com  info.ideal.com    Engadget.com  scenicreflections.com  lih.univ-lehavre.fr  business2press.com  globalswarminghoneybees.blogspot.com  pritisajja.info

53 Intelligent Applications for Web Bio-inspired Web Web Intelligence Searching and Retrieval Searching and Retrieval Knowledge Management on Web Knowledge Management on Web Web Mining Web Mining Agent Based Web Agent Based Web Acknowledgement Artificial Intelligence Artificial Intelligence 53 Created By Priti Srinivas Sajja To the participants and authority of the AICTE sponsored Staff Development Programme on Data Mining, April, 2012 at the L. J. Institute of Engineering & Technology, Ahmedabad. To the participants and authority of the AICTE sponsored Staff Development Programme on Data Mining, April, 2012 at the L. J. Institute of Engineering & Technology, Ahmedabad.


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