# Dept. of CSE, Avanthi College of Engg & Tech, Tamaram, Visakhapatnam, A.P., India

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3 as G(C, R), where C=set of concepts c =n is the total number of concepts available R=(Rij i=1 n,j=1.n,j>i) R is the set of edges in the graph and Rij={r1 ij,r2 ij,,rm ij,m<n} is the set of edges between the concepts i and j. An example of ontology graph is fig.4. Given a particular query continuing specific. Set of key words related to a subset of ontology concepts it is possible to construct a query sub graph GQ. Given an ontology graph G and a query sub graph GQ, it is possible to define a ranking strategy. For a given query Q, for each page P, it is possible to build a page sub graph GQP. A. Relevance And Semantic Relations Here we consider how to apply the methodology for the computation of a page relevance score. Let us imagine here are three keywords k1, k2 and k3 and associated concepts c1, c2 and c3 we now start from k1 c1 after k1 we insert k2 and c2 then finally k3 and c3. Suppose all the above facts are all exist in the pages P1 and p2. Now we want to rank these pages in order to present the user first the pages best fits his or her query. The semantic annotations and the page sub graphs of these pages are illustrated in fig. In fig. 3 (c) in the first page both c1 and c2 are linked with c1 through single relation. In the second page there exist two relations between c3 and c1. But there is no link between c2 and c1. Here it is difficult to compute the best fit, so probability calculation between c2 and c1 and c1 and c3 is calculated. According to the probability theory P (rij, p) = śij/ŋij=ŧij. The efficient algorithm for the identification of a minimum or maximum spanning tree makes use of the so called disjoint set data structure. The algorithm that makes use of this set is called an union find algorithm. Essentially it incrementally builds sets of related objects. The implementation in C is contained in union_find.h andunion_find.c. It is a modified version of the code available fromliterateprogrmas.org. The modifications reflect the pseudocode discussed here. The actual code which perform the the MST and MST is contained in graph.c. It is based on the kruskal s algorithm. The basic functions are void MST (size_t V, Edge *edges, size_t E, Edge **tree, size_t *T) and void MST (size_t V, Edge *edges, size_t E, Edge **tree, size_t *T) Fig.3: (a) An ontology graph. (b) Query subgraph. (c) An example of an annotated page. (d) Page subgraph built upon the given ontology/query. V. Implementation of Ranking Algorithm A. Overall Procedure We now assemble the various steps illustrated in the previous sections to present the overall ranking methodology (whose workflow is depicted in fig. 8). The user starts defining query keywords and concepts. The search engine logic accesses the Web page database, constructs the initial result set including all those pages that contain queried keywords and concepts, and computes the query subgraph. Then, for each page in the result set, the page subgraph is computed. Starting from each subgraph, all page spanning forests (both constrained and unconstrained) are generated and used to compute the page score based on (6). Web pages are associated to relevance classes, and the final (ordered) result set is constructed. B. Spanning Forest Generation Algorithm According to [6], calculating the relevance score for a single page requires considering all the page forests and, for each forest, computing the constrained page relevance score. This requires finding an efficient way for both enumerating all the page forests for a given query and computing the page probability. VI. Experimental Results In this section, the applicability of our technique into real scenarios will be analyzed by conducting two types of evaluations aimed at measuring the performance in terms of both time complexity and accuracy. The time complexity will be compared with that of [10], since our technique could be easily seen as an extension of it. Nevertheless, since the methodology in [10] is not targeted at ranking the result set, the accuracy of results will be compared with that of a traditional search engine like Google. A. Time Complexity The computation of fair results concerning time complexity requires a sufficiently large repository with a significant number of annotated pages. Because of the difficulty of integrating the proposed technique within today s search engines like Google, in which a native semantic layer is actually missing, we chose to estimate the computation time over a synthetic Semantic Web environment. The positive effect of this choice is twofold. On one hand, it is possible to International Journal of Computer Science and Technology 429

4 ISSN : (Print) ISSN : (Online) this work, we propose a novel ranking strategy that is capable of providing a relevance score for a Web page into an annotated result set by simply considering the user query, the page annotation, and the underlying ontology. Page relevance is measured through a probability-aware approach that relies on several graph-based representations of the involved entities. By neglecting the contribution of the remaining annotated resources, a reduction in the cost of the query answering phase could be expected. Despite the promising results in terms of both time complexity and accuracy, further efforts will be requested to foster scalability into future Semantic Web repositories based on multiple ontologies, characterized by billions of pages, and possibly altered through next generation semantic spam techniques Fig. 4 : Ontology used for measuring time complexity. work on as many pages as needed, thus effectively simulating the next-generation Semantic Web repositories. On the other hand, by statistically annotating Web pages, we do not incur in the risk of biasing the result. In order to compare our measures with those of [10], we worked with the same ontology (travel.owl), and we selected the same query (in the query, illustrated in fig. 6, specific keywords and concepts defined in [10] have been replaced with numeric indexes). We automatically generated a Web page database with one million pages, each page containing all the keywords specified in the query. For each page, we constructed a semantic annotation based on the concepts defined in the selected ontology, randomly associating to each keyword one of the concepts in the ontology. We adjusted the statistical parameters so as to obtain a set of approximately 100,000 pages (precisely, 96,843 pages) including at least one of the keywords associated exactly to the concept specified in the query. Finally, we added semantic relations between concepts by uniformly distributing them across pages. In this way, each pair of concepts was linked by a variable subset of the relations associated to that pair in the ontology (each page containing approximately 10 relations). The distribution of concepts and relations in the Web page database for each concept cijci CQ, reports the number of pages ontaining exactly the association keyword/concept (k, c) efined in the query. Moreover, for each couple of concepts ci; cjjci; cj CQ, it reports the number of pages in which oth the concepts are associated to the keywords specified in the user s query B. Accuracy The accuracy of the result set generated is evaluated by running the query hotel-hill station, Ooty. The web page written by the traditional search engine shows that out of scope pages are ranked as very relevant and potentially interesting pages are positioned at the end of the result. But now we manually annotate each page using concepts accommodation, destination, accommodation rating and activity in the travel.owl ontology. It is observed that the ranking is significantly improved. Out of the six entries at least four are to the satisfaction of the user query VII. Conclusion and Futurework The next-generation Web architecture represented by the Semantic Web will provide adequate instruments for improving search strategies and enhance the probability of seeing the user query satisfied without requiring tiresome manual refinement. They mainly use page relevance criteria based on information that has to be derived from the whole knowledge base, making their application often unfeasible in huge semantic environments. In 430 International Journal of Computer Science and Technology ACKNOWLEDGMENTS I like to thank all my friends for their valuable suggestions for this paper. References [1] B. Aleman-Meza, C. Halaschek, I. Arpinar, A. Sheth, A Context-Aware Semantic Association Ranking, Proc. First Int l Workshop Semantic Web and Databases (SWDB 03), pp , [2] K. Anyanwu, A. Maduko, A. Sheth, SemRank: Ranking Complex Relation Search Results on the Semantic Web, Proc. 14th Int l Conf. World Wide Web (WWW 05), pp , [3] R. Baeza-Yates, L. Caldero n-benavides, C. Gonza lez-caro, The Intention behind Web Queries, Proc. 13th Int l Conf. String Processing and Information Retrieval (SPIRE 06), pp , [4] T. Berners-Lee, M. Fischetti, Weaving the Web. Harper Audio, [5] T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web, Scientific Am., [6] S. Brin and L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine, Proc. Seventh Int l Conf. World Wide Web (WWW 98), pp , [7] S. Cohen, J. Mamou, Y. Kanza, Y. Sagiv, XSEarch: A Semantic Search Engine for XML, Proc. 29th Int l Conf. Very Large Data Bases, pp , [8] L. Ding, T. Finin, A. Joshi, R. Pan, R.S. Cost, Y. Peng, P. Reddivari, V. Doshi, J. Sachs, Swoogle: A Search and Metadata Engine for the Semantic Web, Proc. 13th ACM Int l Conf. Information and Knowledge Management (CIKM 04), pp , [9] L. Ding, T. Finin, A. Joshi, Y. Peng, R. Pan, P. Reddivari, Search on the Semantic Web, Computer, vol. 38, no. 10, pp , Oct [10] Y. Li, Y. Wang, and X. Huang, A Relation-Based Search Engine in Semantic Web, IEEE Trans. knowledge and Data Eng., vol. 19, no. 2, pp , Feb [11] R. Guha, R. McCool, E. Miller, Semantic Search, Proc. 12th Int l Conf. World Wide Web (WWW 03), pp , [12] Z. Gyongyi, H. Garcia-Molina, Spam: It s Not Just for Inboxes Anymore, Computer, vol. 38, no. 10, pp , Oct

5 Mr. G.Santosh Kumar received the B.Tech degree from the Department of Information Technology, GMRIT, JNTUniversity, Hyderabad in 2006 and He is currently pursuing M.Tech in the Department Of Computer Science and Engineering, Avanthi Institute of Engineering and Technology, Vishakhapatnam, JNTU university His research interests include Knowledge and Data Engineering Mr. Satya P Kumar Somayajula is working as an Asst.Professor, in CSE Department, Avanthi Institue of Engg & Tech, Tamaram, Visakhapatnam,A.P., India. He has received his M.Sc(Physics) from Andhra University, Visakhapatnam and M.Tech (CST) from Gandhi Institute of Technology And Management University (GITAM University), Visakhapatnam, A.P., INDIA. He published 5 papers in reputed International journals & 5 National journals. His research interests include Image Processing, Networks security, Web security, Information security, Data Mining and Software Engineering. International Journal of Computer Science and Technology 431

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