Thursday, October 12, 2017


Search Engine, Web Intelligence, Multi Agent System, Artificial Intelligence, Knowledge Based System.

Categories and Subject Descriptors
D.3.3 [Programming Languages/IDE]: Java and Netbeans – Agent Classes, Databases, UI Classes.


Searching of relevant information for the end user is getting more and more difficult day by day as the amount of information is increasing that is either available on Internet or in computer systems. So, in this regards advance searching techniques are evolving which are coming up with different ideas to provide the solution for these problems.
However, with the emerging of new trends in the field of search engines and knowledge base systems many efforts are made and technologies are evolve. In this research paper we have discuss a architecture of a knowledge base system which will allow the extracting of information from a data collected from different sources. Combinations of different techniques are used to form the structure which includes the maintaining of profile, activity history and searching hints. We will be also covering some intelligent techniques to perform these tasks.
In this paper we have consider few items to be specific to the structure implementation. The approaches are discussed which are possible to be implemented which we will discuss in our Experiment [5]. The approach and technique which is proposed can be suitable for many situations but here we are approaching for people whose work is dependant on business updated knowledge and information, which could be the people from Marketing, Sales and Higher Management, anytime anywhere.

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Artificial Intelligence, Knowledge Base, Multi Agent System (MAS), Web Intelligence, Searching Techniques, Search Engines.

1.                   INTRODUCTION

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".
In order to classify machines as "thinking", it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception has aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the human brain, made up of billions of neurons, and arguably the most complex matter Abacus Consulting in the universe. Perhaps the best way to gauge the intelligence of a machine is British computer scientist Alan Turing's test. He stated that a computer would deserve to be called intelligent if it could deceive a human into believing that it was human.

1.1                 Web Intelligence and Agent Systems in WIAS

An International Journal is an official journal of Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web and Agent Intelligence. WIAS seeks to collaborate with major societies and international conferences in the fields. Presently, it has established a tie with the International Conference on Web Intelligence and the International Conference on Intelligent Agent Technology. WIAS is a peer-reviewed journal, which publishes 4 issues a year, in both electronic and hard copies. WIAS aims to achieve a disciplinary balance between Web technology and intelligent agent technology.

1.1.1         Web Intelligence

Web intelligence is a combination of web analytics, which examines how website visitors view and interact with a site’s pages and features, and business intelligence, which allows a corporation’s management to use data on customer purchasing patterns, demographics, and demand trends to make effective strategic decisions. As companies expand their reach into the global marketplace, the need to Analyze how customers use company websites to learn about products and make buying decisions is becoming increasingly critical to survival and ultimate success.
With Web Intelligence, we can make better decisions in less time, by turning information into actionable insight at the speed of thought. Web Intelligence is built on our proven, mature BI platform—Business Objects. This ensures that the deployment meets performance demands and supports standardization efforts.
From improving corporate decision-making to sharing information with customers, suppliers, and partners, Web Intelligence delivers self-service insight to everyone who needs it. Business Objects Web Intelligence empowers the users with self-service information access and interactivity, while delivering:

·         Powerful, on-line and offline ad hoc query and reporting
·         Integrated and trusted analysis for all users
·         A tool built upon the most complete, trusted, and agile BI platform

1.1.2         Agent Systems

Agents are entities that are designed to run routine (user driven) tasks and to achieve a proposed setting (or goal) within the context of a specific environment. The difference between an Agent and a traditional software entity is that the latter just follows its designed functions, procedures or macros to run deterministic codes. The former incorporates the ability to practice intelligence by making (autonomous/ semi-autonomous) decisions based on dynamic runtime situations.
Systems using Software Agents (or Multi-Agent Systems, MAS) are becoming more popular within the development mainstream because, as the name suggests, an Agent aims to handle tasks autonomously with intelligence.

1.1.3         Communication Languages for Agent

Agent Communication Language (ACL), proposed by the Foundation for Intelligent Physical Agents (FIPA), is a proposed standard language for agent communications. Knowledge Query and Manipulation Language (KQML) is another proposed standard.

The most popular ACLs are:

v  FIPA-ACL (by the Foundation for Intelligent Physical Agents, a standardization consortium)
v  KQML (Knowledge Query and Manipulation Language)

Both rely on speech act theory developed by Searle in 1960 and enhanced by Winograd and Flores in the 70s and define a set of performatives and their meaning (e.g. ask-one).
To make agents understand each other they have to not only speak the same language, but also have a common ontology. Ontology is a part of the agent's knowledge base that describes what kind of things an agent can deal with and how they are related to each other. [11]

1.1.4         Knowledge Query and Manipulation Language (KQML)

The Knowledge Query and Manipulation Language, or KQML, is a language and protocol for communication among software agents and knowledge-based systems. It was developed in the early 1990s part of the DARPA knowledge Sharing Effort, which was aimed at developing techniques for building large-scale knowledge bases which are shareable and reusable. While originally conceived of as an interface to knowledge based systems, it was soon repurposed as an Agent communication language.
The KQML message format and protocol can be used to interact with an intelligent system, either by an application program, or by another intelligent system. Experimental prototype systems support concurrent engineering, intelligent design, intelligent planning, and scheduling.

KQML is superseded by FIPA-ACL. [11]

As KQML has been out-dated, so in our approach we are using the FIPA-ACL as the communication language between the agents.

1.1.5         Autonomy

Autonomy provides you with a whole suite of different intelligent agents to suit a variety of searching needs. Autonomy is a web-based service, it is a program which needs to be downloaded and installed on your own PC. It then works with web browser to provide searching facilities.

Autonomy agents are trained by typing a few words of interest into a search box provided, then the agents loose on the web and they go off to look for relevant documents. These documents are graded according to their perceived relevance to the topics you have chosen.

Here the approach is that the agent will go to a knowledge base database, where all the information is already present. And once the agent has finished searching it displays a list of result found. Which can then review these sites and accept those that appear to be relevant to your information needs?
Autonomy create a library for the sites which have accepted and use this information to refine its searching the next time you ask it to perform a search on that particular topic. Following the same approach our agent will maintain the history of users to keep the track of the search result so that they can be used in future.

1.1.6         Knowledgebase Systems

 “a knowledge-based system is a program for extending and/or querying a knowledge base.” [14]

“a knowledge-based system as a computer system that is programmed to imitate human problem-solving by means of artificial intelligence and reference to a database of knowledge on a particular subject.” [15]

Knowledge-based systems are kind of intelligent systems that are based on the methods and techniques of Artificial Intelligence. Their core components are the knowledge base and the inference mechanisms. [11]

A knowledge base used within a company could support decision-making and increase the intelligence of the business...

1.1.7           Semantic Web

The Semantic Web is an extension of the current Web that will allow you to find, share, and combine information more easily. It relies on machine-readable information and metadata expressed in RDF.

Organization schemes like ontologies are conceptual; they reflect the ways we think. To convert these conceptual schemes into a format that a software application can process we need more concrete representations...[12]

1.1.8           Resource Description Framework (RDF)

W3C standard XML framework for describing and interchanging metadata. The simple format of resources, properties, and statements allows RDF to describe robust metadata, such as ontological structures. As opposed to Topic Maps, RDF is more decentralized because the XML is usually stored along with the resources. [13]

2.                  RELATED WORK


A web site is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy web sites.
Everyday people retrieve all kinds of information from the web. For example, when shopping online, people find product specifications from web sites like or When looking for interesting DVDs, they get information and read movie reviews on web sites such as or There is no guarantee for the correctness of information on the web.
We propose a new problem called Veracity, i.e., conformity to truth, which find true facts from a large amount of conflicting information on many subjects that, is provided by various web sites. We design a general framework for the Veracity problem, and invent an algorithm called Truth Finder, which utilizes the relationships between web sites and their information. Our experiments show that Truth Finder successfully finds true facts among conflicting information, and identifies trustworthy web sites better than the popular search engines.
There is no guarantee for the correctness of information on the web. Moreover, different web sites often provide conflicting information on a subject, such as different specifications for the same product. Even worse, different web sites often provide conflicting information. [1]

The performance of a Worldwide Web (WWW) server became a central issue in providing a ubiquitous, reliable, and efficient information network for real-time ubiquitous-unified Web information services. For wired Internet using HTML with agents in PCs, and for mobile Internet using WML or mHTML with mobile agents in mobile devices, the management of Web server agents and mobile agents becomes more difficult for real-time Web services.
When browsing information on large Web sites, users often receive too much irrelevant information. The amount of knowledge and information in the Web has been growing tremendously and pushing, in a sense, an already flooded society with knowledge and information; however, searching, in real-time way, the right information in Web portals has become more difficult in another sense due not only to the amount of answers, but also to the inconsistency of those answers provided by various multi-agent portals.
The performance of Web information access for real-time (precisely, soft real-time instead of hard real-time) ubiquitous-unified Web information services, using the unified Web information portal with a cost-effective Web server and intelligent mobile agents. The Web 2.0+ and its applications have been revolutionarily changing and affecting the world in various ways, especially toward the Knowledge and Information Society. The Web server is a role center for unified information services; and the intelligent mobile agents for Web information access have become very important for a user-group in ubiquitous computing environments. For performance of the knowledge-based Web information access through the unified information Web server in a ubiquitous information network.
Via both wired and mobile Internet, real-time ubiquitous-unified Web information services for information access should be considered for their convenience, as well as integrity of consistent information with a real-time requirement in this Information Society and we considered several aspects about the Web server for a user-group using real-time Web services. [2]

Different techniques have been exploited to mine web search query logs for query recommendation, query expansion and query completion. The importance of real-time and interactive phrase suggestions while the query is formulated. An interactive query completion algorithm that suggests the frequent words of the last incomplete word in the query.
Our observation from the AOL query log shows that the average number of items in a query is 2.14, while it is smaller in the University of New Brunswick (UNB) search engine query log which is 1.94. This is often an insufficient number of items for finding the most relevant web pages.
Your Eye, the real-time phrase recommender is introduced that suggests the related frequent phrases to the incomplete user query. The frequent phrases are extracted from within previous queries based on a new frequency rate metric suitable for query stream mining. An advantage of Your Eye compared to Google Suggest, a service powered by Google for phrase suggestion, is described. The experimental results also confirm the significant benefit of monitoring phrases instead of queries. The number of the monitored elements significantly reduces that result in smaller memory consumption as well as better performance. [3]

Web usage mining can play an important role in supporting the navigation on the future Web. In fact detection of common or professional profiles allows browsers and web sites to personalize the user session and to recommend specific resources to the interested people.
Semantic web approach seems interesting for this task. This paper a generic approach for profile detection relying on semantic web technologies. It takes advantages from ontologies, Semantic annotations on web resources and inference engines. Keywords: profile learning, ontologies, annotations, semantic web browsing.
This work is carried out in the framework of the European project Sea life [3]. The objective of Sea life is the design and development of a semantic Grid browser for the Life Sciences, which will link the existing Web to the currently emerging eScience infrastructure.
One of the use cases in Sea life project consists of linking information on biomedical websites to appropriate secondary knowledge (existing ontologies/terminologies, RSS feeds…). This case study will demonstrate how to provide.
The user with additional information on resources He/she is viewing on biomedical websites, using a semantic mapping to appropriate online portals and databases (called targets). In this purpose, the Sea life browser must recognize the user profile in order to select the appropriate ontology and targets. The test scenario of this use case will be tested on the NELI1 (National Electronic Library of Infection) web site which is a digital library dedicated to the investigation, treatment, prevention and control of infectious diseases. [4]

The document deal with the study of mining the web information and making the interconnectivity with those words to find information more relatively.
In analyzing text, there are many situations in which we wish to determine how similar two short text snippets are. For example, there may be different ways to describe some concept or individual, such as “United Nations Secretary-General” and “Kofi Annan”, and we would like to determine that there is a high degree of semantic similarity between these two text snippets.
To address this problem, we would like to have a method for measuring the similarity between such short text snippets that captures more of the semantic context of the snippets rather than simply measuring their term-wise similarity. To help us achieve this goal, we can leverage the large volume of documents on the web to determine greater context for a short text snippet. By examining documents that contain the text snippet terms we can discover other contextual terms that help to provide a greater context for the original snippet and potentially resolve ambiguity in the use of terms with multiple meanings.
Presently, we formalize our kernel function for semantic similarity. Let x represent a short text snippet1. We compute the query expansion of x, denoted QE(x), as follows:
1.       Issue x as a query to a search engine S.
2.       Let R(x) be the set of (at most) n retrieved documents d1;d2; : : : ;dn
3.       Compute the TFIDF term vector vi for each document di 2 R(x)
4.       Truncate each vector vi to its m highest weighted terms
Given that we have a means for computing the query expansion for a short text; it is a simple matter to define the semantic kernel function K as the inner product of the query expansions for two text snippets. More formally, given two short text snippets x and y, we define the semantic similarity kernel between them as:
K(x; y) = QE(x) _QE(y):
We note that K(x; y) is a valid kernel function, since it is defined as an inner product with a bounded norm (given that each query expansion vector has norm 1.0), thus making this similarity function applicable in any kernel-based machine learning algorithm (Cristianini & Shawe-Taylor 2000) where (short) text data is being processed.
Learning Similarity Functions for Record Linkage
Turning our attention to another important problem in measuring similarities, we consider the record linkage task. Record linkage is the problem of identifying when two (or more) references to an object are describing the same true entity.
Comparing Similarity Functions for Making Recommendations in On-line Communities
In addition to web search and comparison shopping, we have also examined the use of similarity measures in online social networks. Social networking sites such as Orkut (, Friendster (, and others have quickly gained in popularity as a means for letting users with common interests find and communicate with each other. [5]

In this paper, a suggestion of intelligent web information system for minimizing information gap in government agencies and public institutions delivering personalized web contents which disadvantaged people can understand and from which they make the more profit in their economic behaviors.
For developing the system, we identify disadvantaged people having a lot of total losses and a high probability for loss per transaction through analyzing transaction data of all markets. Then we identify the difference of information gap between disadvantaged people and the other advantaged people, and redesign the contents of web pages for disadvantaged people to make good a gap of information and to understand it easily.
Therefore, we suggested an intelligent web information system in government for help disadvantaged users make more profit in their economic behaviors. We defined the important issues for developing intelligent web information system in government effectively: design of web contents, personalization, and corresponding to change of market environment. [6]

The explicit customization of software applications is considered a cumbersome task that most noncomputer-skilled end-users cannot afford. Thus, the few existing approaches to this respect have been mainly focused on some domain-dependent support. Further, the traditional desktop customization process cannot be applied straightforward to Web environments.
The complexity of programming and specification languages discourages users even from attempting software customization. Although most applications do not provide much support for customization, some of them allow users to adapt partial aspects of the application to their own needs by selecting predefined options. Desktop applications are usually complex and implemented in structured programming languages. This has traditionally made it difficult to provide easy-to-customize end-user approaches for them.
To face such a challenge, we leverage Model-Based User Interfaces Design (MBUID) approaches (Paterno`, 2001) combined with customization techniques (Macı´as and Castells, 2004). The overall goal is natural development (Berti et al., 2006), which implies that people should be able to create or modify applications by working through familiar and immediately understandable representations to express relevant concepts. In this respect, our main contribution exploits Model-Based User Interface Design (Szekely, 1996) and End-User Development research, combining them by means of an intelligent environment that can infer meaningful information from the user’s modifications.
The approach is based on an expert system where the knowledge is built up progressively, increasing in every user session (i.e. evolutionary approach). [7]

AI planning is the main stream method for automatic semantic web service composition (SWSC) research. However, planning based SWSC method can only return service composition upon user requirement description and lacks flexibility to deal with environment change. Deliberate agent architecture, such as BDI agent, is hopeful to make SWSC more intelligent.
Semantic web service composition (SWSC) is currently one of the most hyped and addressed issue in the Service Oriented Computing. Nowadays, most research conducted fall in the realm of workflow composition or AI planning to build composite web service.
The proposal was of an automatic SWSC enabling method based on AgentSpeak Language. SWSC method alone can only return service composition upon user requirement description and lacks flexibility to deal with environment change. To enable an agent, which is written in AgentSpeak language, to perform SWSC according to composite service description, OWL-S services should be converted to agent plans.
Core SWSC process goes with agent’s intention formation mechanism. To agent’s world, the service set and target service description means plan set and goal event. To services’ world, agent’s intention means service execution sequence. The mapping between the two worlds is OWLS2APS algorithm. [8]

Brand images and reputation are paramount to corporations, especially consumer facing companies. It is extremely easy for a brand to become tarnished or become negatively associated with a social, environmental, or industry issue. This is true especially with the emergence of new forms of media, such as blogs, weblog, message boards, and web sites.
The new media allows consumers to spread information freely and at the speed of thought. By the time publicity has reached the press, it can be too late to protect the brand - only damage control is possible. Recent pet food recall and firing of IMUS both started with blog and message board postings.
COBRA embeds a suite of analytics capabilities to allow effective brand and reputation monitoring and alerting, which are specifically designed for blog and web data mining. In addition, grammars. Both web and blogs have many duplicates.
COBRA also includes techniques for fast and continuous ETL processing for large amount of semi-structured and unstructured data. This is important since blogs and web content tend to be particularly dirty, noisy, and fragmented.
Without special ETL processing, analytics may be meaningless. Web pages may contain banners and advertisements that need to be stripped out. Blogs may contain fragmented sentences and mis-spellings. [9]

Email has been an efficient and popular communication mechanism as the number of Internet users increases. Therefore, email management became an important and growing problem for individuals and organizations because it is prone to misuse.
The blind posting of unsolicited email messages, known as spam, is an example of the misuse. Spam is commonly defined as sending of unsolicited bulk email - that is, email that was not asked for by multiple recipients.
Currently, much work on spam email filtering has been done using the techniques such as decision trees, Naïve Bayesian classifiers, neural networks, etc. To address the problem of growing volumes of unsolicited emails, many different methods for email filtering are being deployed in many commercial products. We constructed a framework for efficient email filtering using ontology. Ontologies allow for machine-understandable semantics of data, so it can be used in any system. It is important to share the information with each other for more effective spam filtering.
It is necessary to build ontology and a framework for efficient email filtering. Using ontology that is specially designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system. [10]

3.                  METHODOLOGY

3.1    Our Approach

Our system is designed in such a way that the user of different platforms can use this application which includes (Desktop PCs, PDA, Smart Phone, Notebook and Tablet PCs). But in our experiment we are using desktop computer as our interface.

 Figure – 3.1 – Architecture

The Server is responsible for run the database and the main Agents including the Server, Profile, Knowledge Base and History, whose functionally is discussed detail [4] Components.

3.2   User Interface & Intelligent Processing

The description of our approach interfaces and processes is as follows:
·         The end user access the application from his desktop computer by start the Client Agent, which connects to the main Server Agent.
·          Once the user starts the application, the application asks for User ID and Password to login on the server.
·         The Client Agent sends the given ID and Password to the main Server Agent which then passes to Profile Agent for checking the login credentials.
·         Once the user is authenticated the Profile Agent fetches the users profile specified in the database to the Server Agent which send back to the Client Agent. If the authentication failed an error message is send by the same route.
·         The Client Agent next screen shows a Search screen which allows the user to search the information from the Knowledge Base (KB) database.
·         And on the same search screen there is another list showing the last 10 histories of the user searches, which is retrieve from the database by the User History Agent, as the user login the screen.
·         As the user start typing on the search line and it reaches to 3 characters the system contact the KB Agent to retrieve the suggestion and provide on the user screen to select the relevant word to the user, in order to avoid searching again and again.
·         The KB Agent queries the KB Database to get the desire result related to the user profile defined which also minimizes the searching options.
·         At the end of the process the user is provided with the narrow searches from the Knowledge database.
·         On the selection of the specific result the detailed information is provided to the user on the same screen below.
·         Here is our experiment in which we are only considering textual information that is being passed in the form of messages, but can be extant to allow viewing of documents.
·         And further more here we have not developed any user Knowledge Database update module, but that can be developed for allowing the updating of knowledge in the database.

Figure – 3.2 – Knowledge Extraction Process

4.                  SYSTEM ARCHITECTURE

Many different techniques are used to implement the concept for providing the user with there relevant information in time on field through different medians with less efforts. To enable this we have used Artificial Intelligence (AI) Agent Based technique, to come up with the solution. The system architecture is as follows:

Figure – 4.1 – Component Diagram

4.1                Client Agent

The client agent is the interface between the user and the KB database, from where user queries to database. The agent login screen once authenticate the search screen provide a search textbox along with the history of user last 10 searches and quick suggestion list. Then the result screen of information for the selected search.

4.2               Server Agent

The server agent is the main authority between the client agents and agents handling the request respected to there job. All the either related to the authentication, search or history request are first send to the main server agent which then pass to the respective agent. And same vice versa from the working agent to the respective client from where the request has been received.

4.3               Profile Agent

The profile agent is responsible for maintaining the user profile and information related to the user. And another task that is performed by this agent is the authentication of users while logging in the system. It receives the login credentials from the server agent and check it with the details define in the database and on validation it send the confirmation to move the user to the search screen and incase of failure it will return a error on the screen.

4.4               KB Agent

The Knowledge Base agent is responsible for interacting with the database performing the search request of the users and returns the relevant search to back to the server agent which will forward to the client agent.
The KB agent will also work as a quick search keyword identifier which will return the possible desire search might be required by the user.

4.5               User History Agent

It is responsible for maintaining the history of user activities, once a successful result is return to the user and viewed by user, that specific result stored and when the user will re-login again next time it will show in the history on the user which is another function of the history agent that on login of any user the list of last successful search. And when the user login back on next time that item and previous all items are shown in the history list. Allow quick access of successful accessed knowledge.

4.6               Database

The database is used to store the entire knowledge base information which will be requiring by users for the extraction of data. The data is being updated by the user that will use a interface to update the information in the database. And the database will also contain the user profile and history details.
The database contain the textual data with the keywords define to that specific data, which will allow to segregate the data with different profile or working.

4.7               Knowledge Updater (User)

The approach which we are carrying is having a user which will be updating the knowledge in the database with all the relevant required details to it including keywords, scope of knowledge e.t.c. And further more the user will be responsible creating and updating the end user profile that will be accessing the knowledge from the database.

5.                  EXPERIMENT


To evaluate the approach which we have proposed in our paper, a multi-agent based system has been developed following the same components and structure which is mentioned above in the paper.

In this experiment we try to test the main theme concept of the structure.

5.1                Populating the History List

On the logging in the list has been populated with the entire last successful search result view by the user. The user will be able to directly access that specific topic from the list. The procedure of populating the list is based on user profile, on success login User ID, is passed to the history agent which will return back a list of content for the previous stored success search till the N records, which is define as 10 the number of successful result to topic to be return back to the user screen.

5.2               Extracting of Suggestion Phrase

The input from the search text-box on the search screen on trying on every character the request to the KB Agent is based to return a list of suggested word. If we assume that C1C2C3 are 3 characters as it reach to third character the request for suggestion is send, and the system check the word in the database and return back a list, but here there is one more thing that restrict the search which in a domain to become more efficient and fast that is the user profile, in which the searching is performed.

Figure – 5.1 – Search Screen (Interface Layout)

5.3               Reviewing the Result List

On the search request the system return a list of matching results, with title and description for that topic. And the user selects its required topics for detailed information.

Figure – 5.2 – Search Result Screen (Interface Layout)

5.4               Viewing the Desire Search

The user select the topic and that specific topic information is displayed to the user and on display the user can marked that result to be in the history list so that it can be access directly next time by user.

Back Search
Update in History
Figure – 5.3 –Result Screen (Interface Layout)

6.                  CONCLUSION AND FUTURE WORK

In this research paper, we have tried to sketch a model for multi agent system that is basically implementing the complete concept. All the communication of messages is being performed trough a server agent which act as a bridge between both ends.
This application or blueprint which we have developed is to show the possibility of implementing such a structure, still so many things can be done to improve and add new features to it, which could allow us to access this application from different platforms and improve the client interface.
In this overall concept of there are many techniques from the combination of which we have form a model, in which is still not using any particular approach to optimized this whole procedure. Still we optimized the searching techniques and following of information. The basic concept which we have given through this paper is that with the combination of approaches which we have followed, we can come up with a smart solution for any system providing knowledge to users either that is search engine or specific to any field.
We will continue our research in this direction to find more loopholes which are currently present and try to expand the module to expand the scope of this approach.

7.                  ACKNOWLEDGMENTS

We would like to thanks our teacher Mr. Muhammad Khalid Khan, for assisting and guiding us through out our research, and our classmates for supporting us.

8.                  REFERENCES

[1]     Truth Discovery with Multiple Conflicting Information Providers on the Web.
Xiaoxin Yin (UIUC) -
Jiawei Han (UIUC) -
Philip S. Yu (IBM T. J. Watson Res. Center) -
KDD’07, August 12–15, 2007, San Jose, California, USA.
Copyright 2007 ACM 9781595936097/07/0008

[2]     Web Access Performance With Intelligent Mobile Agents For Real-Time Ubiquitous-Unified Web Information Services
Yung Bok Kim1, Yong-Guk Kim1, and Jae-Jo Lee2
1 Sejong University
KunJa-Dong, Kwang-Jin-Ku, Seoul, Korea 143-747
2 Korea Electrotechnology Research Institute (KERI),
Uiwang-City, Gyeonggi-Do, Korea 437-808
D.-S. Huang, L. Heutte, and M. Loog (Eds.): ICIC 2007, LNCS 4681, pp. 208–217, 2007.
© Springer-Verlag Berlin Heidelberg 2007

[3]     On Query Completion in Web Search Engines Based on Query Stream Mining
M. Barouni-Ebrahimi and Ali A. Ghorbani
Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
{m.barouni, ghorbani}
0-7695-3026-5/07 $25.00 © 2007 IEEE
DOI 10.1109/WI.2007.78

[4]     Recognizing Professional-Activity Groups and Web Usage Mining for Web Browsing Personalization
Yassine Mrabet1, Khaled Khelif1, Rose Dieng-Kuntz1
1INRIA Sophia Antipolis, 2004 route des lucioles, 06902 Nice, France
{Yassine.Mrabet, Khaled.Khelif, Rose.Dieng}
0-7695-3026-5/07 $25.00 © 2007 IEEE
DOI 10.1109/WI.2007.46

[5]     Mining the Web to Determine Similarity between Words, Objects, and Communities
Mehran Sahami
Google Inc.
1600 Amphitheatre Parkway
Mountain View, CA 94043

[6]     Developing An Intelligent Web Information System For Minimizing Information Gap In Government Agencies And Public Institutions
Tae Hyun Kim a,*, Gye Hang Hong b,1, Sang Chan Park a,2
(a) Department of Industrial Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu,
Daejeon 305-701, Republic of Korea
(b) Dongbu CNI, #28th Floor, Dongbu Financial Center, 891-10 Daechi-dong, Gangnam-gu, Seoul 135-523, Republic of Korea

[7]     Customization of Web applications through an intelligent environment exploiting logical interface descriptions
Jose´ A. Macı´as 1, Fabio Paterno` *
ISTI-CNR, Via G. Moruzzi 1, 56124 Pisa, Italy
Received 4 September 2006; received in revised form 28 June 2007; accepted 8 July 2007
Available online 6 August 2007

[8]     Automatic Semantic Web Service Composition via Agent Intention Execution in agentSpeak
LI Huan - Dep. of Computer Science and Technology, Xian JiaoTong University Dep. of Computer Science and Technology, Dongguan University of Technology
QIN Zheng - Dep. of Computer Science and Technology,Xian JiaoTong University, Key Lab for ISS of MOE, Software School, Tsinghua University
YU Fan - Key Lab for ISS of MOE, Software School, Tsinghua University
Qin Jun - Manchester Business School
YANG Bo - Dep. of Computer Science and Technology,Xian JiaoTong University

[9]     COBRA – Mining Web for COrporate Brand and Reputation Analysis
Scott Spangler, Ying Chen, Larry Proctor, Ana Lelescu, Amit Behal, Bin He,
Thomas D Griffin, Anna Liu, Brad Wade, Trevor Davis
IBM Almaden Research Center
{yingchen, , lproctor, alelescu, abehal, binhe, tdg},
{spangles, annaliu, bwade},

[10]  Spam Email Classification using an Adaptive Ontology
Seongwook Youn, Dennis McLeod
Department of Computer Science, University of Southern California, Los Angeles, CA. USA
Email: fsyoun,

[11]  ACL, KQML, Knowledge Base System
Wikipedia, the free encyclopedia

[12]  Semantic Web

[13]  Resource Description Framework (RDF)

[14]  Free On-line Dictionary of Computing (FOLDOC)

[15]  The Computer User High-Tech Dictionary