International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) is an online Journal in English published bimonthly for scientists, Engineers and Research Scholars involved in computer science, Information Technology and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJETTCS are selected through rigid peer review to ensure originality, timeliness, relevance and readability. The aim of IJETTCS is to publish peer reviewed research and review articles in rapidly developing field of computer science engineering and technology. This journal is an online journal having full access to the research and review paper. The journal also seeks clearly written survey and review articles from experts in the field, to promote intuitive understanding of the state-of-the-art and application trends. The journal aims to cover the latest outstanding developments in the field of Computer Science and engineering Technology.
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 Analysis of Personalized Ontology & Emergence of Web Information Gathering B. Nirupama1, Vijaya Lakshmi Kakulapati2 1,2 Department of Computer Science Engineering, Sridevi Women’s College of Engineering, India an emerging need for a standard ontology that will model Abstract: Ontology is widely used to represent user profiles user profiles; this standard ontology will facilitate the as knowledge description personalized web information communication between applications and serve as gathering. However the information of user profiles reference point when profiling functionalities need to be represents patterns either global or local knowledge base developed. information, according to our analysis many models Over the past decade growth of information available on represents global knowledge. In this paper ontology system is the web gathering useful information from the web has used to recognize and reasoning over user profiles, world become a challenging issue for users. Web users expect knowledge base and user instance repositories. This work also more intelligent systems to gather the useful information compares the analysis of existing system and ontology with other research areas is more efficient to represent from the large size of web related data sources, user Keywords: Ontology, user profiles, Image processing, profiles represent the concept models possessed by users software Engineering when gathering web information. A concept model is implicitly either local or global analysis method is effective for gathering the global knowledge. Multidimensional ontology mining method specificity for SECTION I analyzing the concept specified machine-readable Introduction: Personalization of information access documents. indeed to face considerable growth of data heterogeneity of the roles and needs to the rapid development of mobile system becomes important to propose a personalized SECTION II system able to provide user with relevant information 2. Survey on Ontology: Ontology is a extraction and a need. System must into account the different subtask of information extraction is a type of information characteristics of the user and all contextual situations retrieval is automatically extract structured information that influence his behavior during his interaction with from unstructured relevant concepts and relations from a information system. A generic model of profile access given data sets from ontology. It retrieves data based on according to which the personalization system is the local or global does not consider on the primary key articulated based mainly on profiles context user’s gathers data based on the user name in local profile if two preferences. Profiles are knowledge containers context persons have same name it will retrieve both the defines a set of parameters that characterize the information. For capturing the user information needs environment of the system user preferences represent the user profiles were used in web information gathering, expectations of the user. user profile is a collection of data associated to a specific Ontology is best the candidate for representing knowledge personal data associated to a specific user. A profile refers about users to have a shared understanding between to the explicit digital representation of a person’s identity people or software agents of terms and their relations a can also consider as the computer representation of a user controlled vocabulary. Ontologies have been proven and model. User profiles are categorized into interviewing effective information means for modeling a user context semi-interviewing non-interviewing, interviewing profiles can be very useful tool because they may present an are considered perfect for user profiles they are acquired overview of the domain related to a specific area of by using manual techniques such as questionnaries interest and used for browsing query refinement, provides interviewing users and analyzing use classified training rich semantics for humans to work with required sets. Users read each document and gave a positive or formalism for computers to perform mechanical negative advice to the document against a given topic processing. Ontology is used to model the semi-interviewing use profiles are acquired by semi- user profile has already been proposed in various automated techniques with limited user involvement applications like web search ,  and personal usually provides users with a list of categories and ask information management . However, up to this point, user for interesting categories example is the web training ontologies modeling user profiles are application-specific, set acquisition model introduced by Tao et al which with each one having been created specifically for a extracts training sets from the web based on user feedback particular domain. Taking into account the continuing categories. Non interviewing techniques do not involve incorporation of ontologies in new applications, there is Volume 1, Issue 2 July-August 2012 Page 225 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 users at all but ascertain user interests instead acquire 3.3.Ontology in Software Engineering:Modelling user profiles by observing user activity and behavior. ontology is a tedious task always important to demonstrate can gain by applying ontologies in software SECTION III engineering, the current advent of logic based formalisms in the context of the semantic web effort is an important 3.1. Interaction Between Semantic web and factor. Activities by the W3C and others have helped to Ontology: Semantic web mining aims to combine the flesh out standards like RDF or OWL receive increasing development of semantic web and web mining, web attention by tool builders and users. Important factor is mining extracts information from the content of the pages the flexibility of ontologies with information integration its structure of relationships and users browsing records. as a major use case, ontologies are well to combine Very important portion of semantic web is the ontologies information from various sources and infer new factors represented as a set of concepts and their relevant and also the flexibility. Further promoted by the"web"- interrelations for certain domains of knowledge focus of current ontology approaches due to the fact that Sentiment analysis is opinion mining is responsible for software systems also get increasingly web-enabled and classifying words, texts or documents of opinion and must thuscope with data from heterogeneous sources that works in tagging and their components which indicate if may not be known at developmenttime, software the expression is positive negative or neutral and in the engineers seek technologies that can help in this field of the subjectivity of texts as well. situation. Thus,experts in the field like Grady Booch are Web intelligence is research aimed at exploration of the expecting semantic web technology to beone of the next fundamental interactions between artificial intelligence big things in the architecture of web-based applications advanced engineering and information technology is a . Also, theweb makes it easier to share knowledge. general term referring to new area example such as Having URIs as globally unique identifiers,it is easy to informatics of the brain, IA human level and classics such relate one’s ontology to someone else's conceptualization. as engineering knowledge representation planning This in turnencourages interoperability and discovery data extraction. reuse.Regarding more Software Engineering-specific 3.2. Image processing Ontology: Ontology widely advantages, ontologies makedomain models first order used for designing high level scene interpretation that citizens. While domain models are clearly driving the provides the primitives and the concepts at the physical coreof every software system, their importance in current level correspond to the effects of the acquisition Software Engineering processesdecreases after the components on the digital image representation analysis analysis phase. The core purpose of ontologies is by of the various components of a standard acquisition definition theformal descriptions of a domain and thus system lighting environment optical system sensor analog encourages a broader usage throughout thewhole to digital converter and storage gives the list of their Software Engineering lifecycle. possible effects example such as sensor optical system can generate illumination geometry and blur defects on images. SECTION IV 4.1. Knowledge base:The world knowledge base must cover an exhaustive range of topics, since users may come from different backgrounds. The structure of the world knowledge base used in this research is encoded from the LCSH references. The LCSH system contains three types of references: Figure shows the effects generated on image 1. Broader term- The BT references are for two representation by various components of a standard image subjects describing the same topic, but at different levels acquisition system provides the physical level of the of abstraction (or specificity). In our model, they are ontology. encoded as the is-a relations in the world knowledge base. 2. Used-for- The UF references in the LCSH are used for many semantic situations, including broadening the semantic extent of a subject and describing compound subjects and subjects subdivided by other topics. When object A is used for an action, becomes a part of that action (e.g., “a fork is used for dining”); when A is used for another object, B, A becomes a part of B (e.g., “a Image retrieval results identifies the nine categories of wheel is used for a car”). These cases can be encoded as concepts blur, noise, colorimetry, illumination, geometry, the part-of relations. photometry, sampling,quantization and storage only 3. Related term- The RT references are for two primitives that have a genuine manifestation in the input subjects related in some manner other than by hierarchy. images should be provided. Volume 1, Issue 2 July-August 2012 Page 226 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 They are encoded as the related-to relations in our world fact that only users know their interests and preferences knowledge base. perfectly. Baseline Model demonstrated the non- 4.2. Ontology Learning Environment: The subjects of interviewing user profiles, a user’s interests and user interest are extracted from the WKB via user preferences are described by a set of weighted subjects interaction. A tool called Ontology Learning learned from the user’s browsing history. These subjects Environment (OLE) is developed to assist users with such are specified with the semantic relations of superclass and interaction. Regarding a topic, the interesting subjects subclass in ontology. When an OBIWAN agent receives consist of two sets: positive subjects are the concepts the search results for a given topic, it filters and re-ranks relevant to the information need, and negative subjects the results based on their semantic similarity with the are the concepts resolving paradoxical or ambiguous subjects. The similar documents are awarded and re- interpretation of the information need. Thus, for a given ranked higher on the result list. Web modelwas the topic, the OLE provides users with a set of candidates to implementation of typical semi interviewing user profiles. identify positive and negative subjects. These candidate It acquired user profiles from the web by employing a web subjects are extracted from the WKB. Who are not fed search engine. The feature terms referred to the back as either positive or negative from the user, become interesting concepts of the topic. The noisy terms referred the neutral subjects to the given topic. to the paradoxical or ambiguous concepts with 4.3. Ontology mining: Ontology mining discovers disadvantages such as the using web documents for interesting and on-topic knowledge from the concepts, training sets has one severe drawback: web information semantic relations, and instances in ontology. Ontology has much noise and uncertainties. As a result, the web mining method is introduced: Specificity and user profiles were satisfactory in terms of recall, but weak Exhaustively. Specificity (denoted spe) describes a in terms of precision. There was no negative training set subject’s focus on a given topic. Exhaustively (denoted generated by this model compare to this, world exh) restricts a subject’s semantic space dealing with the knowledge and a user’s local instance repository (LIR) topic. This method aims to investigate the subjects and are used in the proposed model. An LIR is a user’s the strength of their associations in ontology. In User personal collection of information items. From a world Local Instance Repository, User background knowledge knowledge base, we construct personalized ontologies by can be discovered from user local information collections, adopting user feedback on interesting knowledge. A such as a user’s stored documents, browsed web pages, multidimensional ontology mining method, Specificity and composed/received emails. and Exhaustively, is also introduced in the proposed model for analyzing concepts specified in ontologies. The Algorithm to be applied is given below users’ LIRs are then used to discover background knowledge and to populate the personalized ontologies. Proposed analysis have benefits Compared with the TREC model, the Ontology model had better recall but relatively weaker precision performance. The Ontology model discovered user background knowledge from user local instance repositories, rather than documents read and judged by users. Thus, the Ontology user profiles were not as precise as the TREC user profiles.The Ontology profiles had broad topic coverage. The substantial coverage of possibly-related topics was gained from the use of the WKB and the large number of training documents. Compared to the web data used by the web model, the LIRs used by the Ontology model were controlled and contained less uncertainties. Additionally, a large number of uncertainties were eliminated when user background knowledge was discovered. As a result, the user profiles acquired by the SECTION V Ontology model performed better than the web model. 5. Comparative Study:The TREC model was used to demonstrate the interviewing user profiles, which SECTION VI reflected user concept models perfectly. For each topic, 6. CONCLUSION TREC users were given a set of documents to read and The proposed ontology system provides a solution to judged each as relevant or non-relevant to the topic. The emphasizing global or local knowledge in a TREC user profiles perfectly reflected the users’ personal computational model and applied to the design of web interests, as the relevant judgments were provided by the information gathering systems. The model also has same people who created the topics as well, following the extensive contributions to thefields of Information Volume 1, Issue 2 July-August 2012 Page 227 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 Retrieval, web Intelligence, RecommendationSystems, and Information Systems and reviews the ontology with B.Nirupama pursuing M.Tech software engineering, image processing retrieval Computer Science Engineering from compares with previous model. Our work extends, will Sridevi Women’s college of investigate the methods thatgenerate user local instance Engineering B.Tech Information repositories to match therepresentation of a global Technolgy from JayaPrakesh Narayana knowledge base. The presentwork assumes that all user College of Engineering. Her research local instance repositories havecontent-based descriptors areas include Data mining Cloud Computing. referring to the subjects, however,a large volume of documents existing on the web maynot have such content- Vijayalakshmi Kakulapati Ph.D based descriptors. M.Tech currently she is the Head of the Deaprtment of Computer Science REFERENCE Engineering and having 20years of Academic Experience. Her research  V. Katifori,, A. Poggi,. M. Scannapieco, T. Catarci, & areas includes Information Retrieval Y. Ioannidis (2005). OntoPIM: how to rely on a Systems, Web Mining, Operating Systems, Unix personal ontology for Personal Information Administration, attended National Conference and Management. In Proc. of the 1st Workshop on The published more than 15 International Journals and life Semantic Desktop. member of various technical bodies like IEEE ACM CSI ISTE.  S. Lawrence, (2000). Context in web search. IEEE Data Engineering Bulletin, 23(3):25-32  J. Trajkova, S. Gauch, Improving Ontology-based User Profiles, Proc. of RIAO 2004, University of Avignon (Vaucluse), France, April 26-28, 2004, pp. 380-389  Amato G., Staraccia U., “User profile modellin and applications to digital libraries”, Proceedings of the 3rd European Conference on Research and avanced technology for digital libraries, p. 184-187, 1999.  Bouzeghoub M., Kostadinov D., L’art et définition d’un modèle flexible de profils, Actes de la 2ème conférence en personnalisation de l’information : aperçu de l’état de recherche d’informations et applications CORIA’2005, Grenoble, France, 2005  C. Hudelot, N. Maillot, and M. Thonnat. Symbol Grounding for Semantic Image Interpretation: From Image Data to Semantics. In IEEE Int. Workshop on Semantic Knowledge in Computer Vision (ICCV), pages 1–8, Beijing, China, 2005.  J. Hunter. Adding Multimedia to the Semantic Web – Building an MPEG-7 Ontology. In Int. Semantic Web Working Symposium (SWWS), pages 261–281, Stanford, CA, 2001.  Oberle, D.: Semantic Management of Middleware, Volume I of The Semantic Web and Beyond Springer, New York (2006)  Mayank, V., Kositsyna, N., Austin, M.: Requirements Engineering and the Semantic Web, Part II. Representation, Management, and Validation of Requirements and System-Level Architectures. Technical Report. TR 2004-14, University of Maryland (2004)  Decker, B., Rech, J., Ras, E., Klein, B., Hoecht, C.: Selforganized Reuse of SoftwareEngineering Knowledge supported by Semantic Wikis. In: Proc. of Workshop on SemanticWeb Enabled Software Engineering (SWESE). November (2005) Volume 1, Issue 2 July-August 2012 Page 228
Pages to are hidden for
"Analysis of Personalized Ontology & Emergence of Web Information Gathering"Please download to view full document