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A comprehensive analysis on semantic web by asif mushtaque
 

A comprehensive analysis on semantic web by asif mushtaque

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A Web mining based on semantic web is helping to ...

A Web mining based on semantic web is helping to
improve the levels of web services as well as addressing
methods of current web services which are supported by the
lack of semantic problem. Web data mining which is based on
semantic is a fusion of web mining and semantic web. The pre
knowledge of semantic web makes web
mining easier to achieve, but it also helps to improve the
efficiency of web mining. In this paper, we give a brief
description of the related knowledge of semantic web and web
mining and then discuss the web mining based on semantics.
Finally we try to propose a web mining model based on
semantics under the framework of agent.

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    A comprehensive analysis on semantic web by asif mushtaque A comprehensive analysis on semantic web by asif mushtaque Document Transcript

    • A Comprehensive Analysis on Semantic Web Mining Shahnawaz Hussain Anu Panwar M.Tech(CS&E) M.Tech(CS&E) School of Computing Science and Engineering School of Computing Science and Engineering Galgotias University, Greater Noida, U.P. Galgotias University, Greater Noida, U.P. Md Asif Mushtaque Harsh Dhiman M.Tech(CS&E) M.Tech(CS&E) School of Computing Science and Engineering School of Computing Science and Engineering Galgotias University, Greater Noida, U.P. Galgotias Abstract: - A Web mining based on semantic web is helping to improve the levels of web services as well as addressing methods of current web services which are supported by the lack of semantic problem. Web data mining which is based on semantic is a fusion of web mining and semantic web. The pre knowledge of semantic web makes web mining easier to achieve, but it also helps to improve the efficiency of web mining. In this paper, we give a brief description of the related knowledge of semantic web and web mining and then discuss the web mining based on semantics. Finally we try to propose a web mining model based on semantics under the framework of agent. Keywords: Web Mining, Web usage mining, Semantic web, ontology, Agent, web content mining, web structure mining, web mining model. 1. INRODUCTION Because of the enormous growth in the development and applications of the internet, the web has become an effective tool for the exchange, sharing of information and for collaborative work. People’s awareness and frequent usage of web promotes the development of this technology and it also provides the rapid growth of web information resources. However, in the distributed web system there is always a flood of information resources, which provide the conveniences to the people to bring the information on the web at the same time, but it also makes the network very difficult to in depth application. On the other hand, a user is only concerned about small chunks of information available on the web and however a user is not interested in the remaining of the information data available on the web, because the required search results will be submerged by the keywords based traditional search engines. While on the other side most of the data available on the web is unstructured, which shows that traditional results of data mining will be unsatisfactory. By using semantic information we should improve the capacities of internet which helps to provide better services for human. Semantics information which is processed by machine sometimes called machine- processable semantics which can be used with the software products which is quite intelligent such as Agents which interacts effectively [1]. Semantics based web mining is a collaborative fusion of web mining and semantic web, which enhances the level of intelligence of access to information provided on the internet [2]. 2. CLASSIFICATION OF WEB MINING AND KNOWLEDGE OF SEMANTIC WEB A. Web mining: Web mining is defined as [3]: Extract interested, useful patterns and implicit information from the web resources and their behavior. In general, web mining can be classified into three different parts [4]: (a) web content mining (b) web structure mining and (c) web usage mining. Figure (a) shows the web mining classification: University, Greater Noida, U.P. 1088 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) IJERT IJERT ISSN: 2278-0181 www.ijert.orgIJERTV3IS041227 International Journal of Engineering Research & Technology (IJERT)
    • Figure (a) Classification of web mining i) WEB CONTENT MINING: It is used to extract the image, text, information and other knowledge component of the web content. For example: which site sells accessories? Which pages are in French? Which pages introduce the videos or introduce news? Intelligent agents, Search engines and some recommend using content mining to help the authentic user in the very vast and diverse network of space to find the significant and important content. Web content mining has two policies: Page text mining, which process results for search engine query to the next level and further so on to fetch some more precise and useful information. ii) WEB STRUCTURE MINING: It is used to extract the information about the customer which uses the browser and use the page links. It fetches out interested patterns from the web. Such as which pages are accessed by the client? Time duration to spend on each page? What next click on? What are the routes of entry and exit? WWW each server saves or retains the log of web access, recording information for the user interaction and access. Analysis of these data can help to understand the behaviour of different users who wants to access the data, which helps to improve the structure of the site as well as it provide user with personalized services. B. Semantic Web: The fundamental idea of the semantic web [5] is to extend the web in a certain degree with the additional to the classical approach of HTML pages with machine-understandable data. So that on the web, the data are not only used for display but also it can be understood by the machine so as to improve the quality of the information services and find out a variety of new, intelligent information services. If in case the information or knowledge that reflects the link between data and applications are embedded in a variety of discriminated information sources in a transparent manner to user, Web pages, database, procedures that will be able to link up through the agent and each other collaborate. We know that semantic web is primarily conceptualised and formalized by Tim Berners-Lee and according to his concept, the semantic network composed of seven levels which Constructs seven layered architecture [6]. As shown in the table 1. The first layer of URI and UNICODE is the basis layer which constructs the structure of the entire system. Unicode [7] is mainly responsible for processing resources encoding, URI is liable for resource identification, which permits precise retrieval of possible information. The second layer of XML+ NS (Namespace) + XML Schema[7] is liable for representing the content and structure of data from the linguistic to separate the performance format, the structure of the data and the content of the information on the network form through the use of standard format language. The third layer of RDF + RDF schema [7], which provides a semantic model which is used to describe the information provided by the Web and type. The fourth layer ontology vocabulary [7] is liable for the description of shared knowledge and it also describes the semantic relationship between the various kinds of information to reveal the semantic between the information itself and information. The fifth layer is the logic layer [7] which is responsible for supplying axioms and interference principles to provide the basis for the services which is considerably intelligent. The last two layers which are liable for providing authentication and trust mechanism. Digital signature and encryption technology is used to detect changes in the document situation is a mean to enhance the security of the Web. This gives the hierarchical structure of the enhanced functionality. XML, RDF(S) and the ontology are its basis in the Semantic Web Web mining Web Structure Mining mining Web Content Mining Web Usage mining PageCo ntent Mining Search results for Mining Usage Mining Personal use of tracking 1089 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) IJERT IJERT ISSN: 2278-0181 www.ijert.orgIJERTV3IS041227 International Journal of Engineering Research & Technology (IJERT)
    • architecture. The stratification of the Semantic Web’s technical support system imprint with the three core technologies. They completely support the semantic description for the information of their network and their knowledge, to play a key role in achieving the semantic-level knowledge sharing and knowledge reuse. Layers Name Description Layer1 Unicode and URI The semantic web based: Unicode processing resources to encoding, URI negative responsible for identification of resources Layer2 XML+NS+XML schema Use to represent the content of data and their structure Layer3 RDF + RDF schema Use to describe web resources and their types Layer4 Ontology vocabulary Describe the various type of resources and relationship between these resources Layer5 Logic In the above 4 layers operate on the basis of logical reasoning Layer6 Proof According to logic, it is used to verify statements in order to draw conclusions Layer7 Trust The establishment of a trust relation between user Table 1 Semantic web architecture The semantic Web [8] is sometimes also called as Web 3.0, this semantic Web is based on the RDF which stands for resource description framework to integrate a diversity of applications of XML- syntax, the uniform resource identifier (URI) is used as naming mechanism. The semantic Web is nothing but the extended version of current web not a new web. The research focus is to emphasis on how the information can only be changed from the form that a computer can read in the form that a computer can understand and deal with it, that is with the semantics, so that the computer and people can simulate and work together. Web resources (such as web services, web pages) for the use of ontology annotation are prominent prerequisite requirement to attain the goal of semantic web. Ontology is proposed in the fourth layer of Tim Berners-Lee[9] proposed the semantic web seven tiered architecture, which focuses to capture the information or knowledge in related fields, which gives a general understanding of knowledge in this zone of semantic Web to determine the field of co- sanctioned vocabulary, and also gives a clear definition between the words and the interrelationships of words, according to the relationship between the concept which describe or explains the semantics of the concept. Semantic annotation using ontology is explained by experts support the content can be understood by users and machines, as compared with general users, this is indicated top down classification. Semantic web which can be viewed as a new generation of information infrastructure is a new distributed platform which is quite intelligent and it is based on semantic information processing. 3. WEB MINING BASED ON SEMANTIC NETWORK Semantic mining [10] is a sequential analysis of information resources and questions of user by IT&T (Intelligence Theory and Technology), through mining its deep semantics, In order to express complete and accurate knowledge resource and user needs, and then in various distributed, data warehouses, heterogeneous databases, knowledge base to search, at last, mined information in intelligent processing to return the most appropriate results of the semantic retrieval mechanism. Semantic based web data mining combines semantic that is extracted from existing web data extraction or existing semantic structures with web mining. Web mining results help to construct the semantic web, web mining knowledge makes easy to achieve and improves the effectiveness of web mining. Semantic based web mining we can be categorized into semantic web content mining, semantic web structured mining and semantic web usage mining categories. Low High 1090 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) IJERT IJERT ISSN: 2278-0181 www.ijert.orgIJERTV3IS041227 International Journal of Engineering Research & Technology (IJERT)
    • Ontology learning Data miningSemantic filtering Storeget Ontology Add Ontology query Ontology generation Ontology Application  Semantic web content and structure mining. Content and structure of the tangled, it vanished the differences between content mining and structure mining, semantic web content and structure mining are collectively referred here to make the complete package. The traditional relevant technique for relational data mining can be transferred to the semantic web content and structure mining.  Semantic Web usage mining. In the semantic web environment, we can provide a lucid semantics to user and their behavior and the body of information based on the log file of semantic ontology knowledge. On the basis, excavation shown to be effective in establishing the users collecting in the same interest, which provide users with ontology based personalized view to improve the web usage mining results. Agents [11] are an intelligent software entity, which is capable to complete spontaneously a specific function and can be related to Agent communications under certain circumstances. The Agent is generally social, autonomous, active and passive response to their own adaptability and mobility. The accuracy of information retrieval can be improved by intelligent agent; it can also complete the intelligent reasoning’s task according to the semantic information on the web. So now Agent technology has been widely used in building an intelligent system. Model of Semantic Web mining under the framework of Agent. According to the pre-mentioned knowledge, to better understand the combination of the semantic network and web mining techniques we can create a semantic web mining model under the framework of agent [12]. This model is divided into five steps for their completion. Figure (b) shows the Semantic web mining model under the framework of agent, Figure (b) Model of Semantic Web mining under the framework of Agent. The first step: In the starting, you need to construct an initial ontology. To construct an initial ontology first need to obtain the necessary set of atomic concepts, we use clustering algorithm to fetch the document from the Web; and then get this concept hierarchy by a variety of different ways. Primary way is to utilize the knowledge acquisition methods generate, such as ONTEX (ontology Exploration) which input a collection of concepts sets relying on knowledge acquisition techniques for detection of properties, and then output the concept collection of above level. Secondary way, in this we can use many of the ontology models that the current ontology models that the current ontology researchers have developed. These consist both general knowledge ontology model description and a specific description of knowledge in the field. Ontology model mesh up the knowledge of experts in the field constructs a conceptual level (initial ontology). The ontology level will be preserved in the ontology library system to RDF Clustering RDF Database Semantic web mining Resource acquisition Ontology Agent Web page Ontology library 1091 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) IJERT IJERT ISSN: 2278-0181 www.ijert.orgIJERTV3IS041227 International Journal of Engineering Research & Technology (IJERT)
    • provide support for the next level of work. The second step: resource acquisition module that gathers task related data sets according to received tasks directions by ontology agents from a web mining. Generally this step is necessary. Because the data set on web is very dispersed, dynamic and generally contain inconsistent data, whether the collection of data is good or bad will have a direct percussion on the result of web mining. The third step: RDF clustering module obtains ontology clustering learning of the data that resource acquisition module has collected. The resource nodes of closest characteristic will be getting together in the RDF data repository. The fourth step: Data preserves in the RDF data repository are fetched by the semantic web mining module and the mining results are provided to ontology agent. The fifth step: The result obtained from the semantic Web mining module for semantic refining and clustering of processing completed by Ontology Agent, to improve the significance of return information and also ontology learning can take leverage of semantic web mining modules to carry out the amplification and modification of ontological knowledge. 4. CONCLUSION AND FUTURE SCOPE In this paper, we briefly introduces the concepts of web mining and Semantic web-related knowledge, then we describes the integration of the two— Semantics based on Web mining , and proposes a semantic web mining model under the framework of agent, Which provides the build process and brief description of each and every module function. Due to the immaturity of the relevant techniques, as well as various aspects of the limitations, this paper is not a concrete realization of the model, which will in future work remains to be further studied. REFERENCES [1] O. Mustapaşa, A. Karahoca, D. Karahoca and H. Uzun- boylu, ―Hello World, Web Mining for E-Learning,‖ Pro-cedia Computer Science, Vol. 3, No. 2, 2011. [2] H. Liu, ―Towards Semantic Data Mining,‖ Proceedings of the 9th International Semantic Web Conference, Shanghai, 7-11 November 2010. [3] Qudamah K. Quboa, Mohamad Saraee ―A State-of-the-Art Survey on Semantic Web Mining,‖ http://dx.doi.org/10.4236/iim.2013.51002Published Online January 2013 (http://www.scirp.org/journal/iim) [4] Faustina Johnson, S. K Gupta ―Web Content Mining Techniques: A Survey,‖ International Journal of Computer Applications, Volume 47– No.11, June 2012 [5] K. Sridevi, Dr. R. Umarani, ― A Survey of Semantic based Solutions to Web Mining,‖ International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 [6] Eddie Moench, Mike Ullrich, Hans Peter Schnurr, Juergen Angele, ―Semantic Miner: Ontology Based Knowledge Retrieval‖, Journal of Universal Computer Science, Vol.9, No.7 (2003), 682- 696. [7] http://www.semantic-web-book.org [8] http://www.w3.org/2001/sw [9] Berner-Lee, T.,Hendler, J., and Lassila, O., The semantic web, Scientific, American, USA, 2001 [10] Zhang Hui, ed, ―ontology based Semantic Web Mining Technology.‖ Computer development and applications, 2009,2 [11] Aarti Singh, ―Agent Based Framework for Semantic Web Content Mining,‖ International Journal of Advancements in Technology http://ijict.org/ ISSN 0976-4860 Vol. 3 No.2 ,April 2012 [12] Usha Venna , K Syama Sundara Rao, ― Semantic-Based Web Mining Under the Framework of Agent,‖International Journal of Innovations in Engineering and Technology (IJIET) Vol. 3 Issue 2 December 2013 ISSN: 2319 – 1058 1092 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) IJERT IJERT ISSN: 2278-0181 www.ijert.orgIJERTV3IS041227 International Journal of Engineering Research & Technology (IJERT)