IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 287- 293

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Crawling of Tagged Web Resources Using Mapping Algorithm C. Saranya

R.S. Ramya

Department of Computer science & Engineering, Anna University [email protected]

Department of Computer science & Engineering, Anna University [email protected]

Abstract— The crawler is used to take out the relevant content from the web resources, the crawler is known as focused crawler. The relevant information is extracted from the SBS (Social Bookmarking Site). The social site which confesses the web user to stores their choices and the quest results of the concerned topic. A bookmarked page contains the relevant content and also some irrelevant content. While bookmarking the page, it contains irrelevant information, which must predict and extract only the relevant information is the major challenge. For overcoming the challenge uses the focused crawler, for expanding the quest topic and also for determining the semantic relevance’s between the tags utilizes the domain specific Concept Ontology. The mapping algorithm is used to find the relevant information from the bookmarked pages. This algorithm is to improve the performance of retrieving the relevant information and accuracy also gets improved. By using this algorithm the page count and word count are analyzed. The page relevance, mapping relevance and the similarity between the pages are analyzed while retrieving the relevant information based on keyword by using mapping algorithm. Keywords— Focused crawler, ontology, ontology-matching, mapping algorithm, RDF I. INTRODUCTION In this world, the major role of the internet is the new information is added in the web periodically. The emerging search engine is the semantic search engine, which is based on the keyword based search this searches the information based on the meaning of the keyword given. The search engine enables the user to share and reuse the needed content. The main goal of this web is to allow the user to find the information, for combining the information and also for sharing the information. Fig 1 shows the major components of the semantic web are RDF (Resource description framework), ontology, ranking and prioritization, Crawler, and Knowledge database (Annotated database) and Ontology This search engine uses web crawler which is also known as web scutter. Web scutter is to find the hyperlinks in web page. Search engine uses the web scutter for updating the web content with other content. This copies the whole web content. The scutter starts searching from the seed points and visits and identifies the links in that web page. After identifying the links it adds those links to the seed, this is done recursively based on crawler policies. The crawling policies are selection policy, re-visiting policy, politeness policy and parallelization policy [4].

RDF (Resource Description Framework)

Annotated/Knowledge database

Crawler

Ontology

Ranking & Prioritization

Fig 1. Components of semantic web

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 287- 293

A. Focused crawler Focused crawler is designed to retrieve the needed information from the web using the keyword based search. The result of the focused crawler is to select information based on the search topic from the web resources this is done by expanding the search topics [1]. This analyses the boundary of crawl and also to find the most relevant pages and the irrelevant web pages by using the links during crawling. The main goal of focused crawler is to evaluate the relevance between the web resources and to identify the nodes those are more relevant to the search topic. This starts crawling from the root set to get the relevant pages; it does not lose its way of searching. The focused crawler is robust and the performance of this is mainly based on the abundance of the links [3]. Focused crawler is divided into: 1. Classic focused crawler [1]: this uses the knowledge about the search topic for determining the relevance between the tagged elements. The model used for focused crawler is the Vector Space Model. This downloads only the highest priority link during searching the interested pattern. 2. Learning focused crawler [1]: this crawler follows the pre-defined guidelines for assigning the priorities. It uses the crawling guidelines. The guidelines are updated periodically and used for crawling. Context graph and hidden markov model are the models s used for computing it. 3. Semantic crawler [5]: this uses social web and semantic knowledge for retrieving relevant web pages. This is modified classic focused crawler. B. Ontology Ontology is defined for describing the relationship between the search topics. This is commonly used for representing knowledge, retrieval of information, understandings of natural languages and web services [2]. Ontology is defined as set of primitives for knowledge. Ontology is for determining the relation between the different concepts. The ontology mapping is the challenging factor in ontology. The main aim of ontology is to share the understanding of information. This is to analyses the domain knowledge, assuming the domain, then reusing the domain knowledge and also for separating domain knowledge from the operational knowledge [2]. The ontology is written by OWL (Web Ontology Language). The ontology contains many sublanguages like Web Ontology Language lite, Web Ontology Language Full and Web Ontology Language DL. C. Ontology-Matching Ontology matching is the challenging factor in otology which specifies the entities of needed information in domain. Thos may be in terms of attributes for classes, which defines the concepts, object instances, properties this finds the association and the values in the form of data types in ontology. Onto-matching is to derive the adjustment between the ontologies. Ontology-matching is also known as onto-match or onto-matching or ontology-map. Onto-matching explains the semantic properties between the concepts in different ontologies by using many formal languages like OIL, DAML+OIL and RDF [6]. Ontology deals the working of mapping pattern matching the element with same meaning between different ontologies it reflect the internal structure. Assume two different ontology l and m with entities l’ and m’. By using the entities find the coherence between the elements based on the same meaning and also find the equivalence between the elements [2]. D. Mapping Algorithm Ontology mapping were created by a merging ontology process. This mapping not only exists between local and global ontologies rather than merged or aligned between the local ontology. Mapping algorithm is the capable of containing existing ontology to the other ontology as a training set of data. This deals with the trained data for improving the accuracy and the performance. In this the information selected from ontology are selected and it can be used for finding the relevance of web pages based on the learner’s needs. This mapping is applicable to ontologies with same domain which is done by ontology merging. In mapping algorithm, first set the start data and the ending data. Based on this it starts searching for the information. II. RELATED WORK This section deals with the discussion about the focused crawler. The focused crawler is classified into classic and learnable focused crawler. Chakrabarti S. et.al [3], this paper deals with focused crawler, is used to describe a hypertext resource discovery system. The focused crawler analyses the crawl boundary to find the relevant links and this eliminates the irrelevant link information. This paper achieves the goal-directed crawling, they designed mining programs they are Classifier and Distiller. Classifier is to grade the relevance’s in the search topic. Distiller distinguishes the nodes which hold relevant information. Focused crawler is sturdy and it does not lose its way while searching for the relevant link. Batsakis et.al [4], this paper proposes learnable focused crawler by HMM. The state-of-the art crawler uses web content and the link information to retrieve the relevant information. This used for estimating the relevance between the search topics. The baseline implementation was used in this crawler for providing the unbiased evaluation framework. This resulting performance increases by achieving the relevant information through web content and the link information. The anchor text is used to find the priorities of the web page.

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 287- 293

The variations of the focused crawler [7] apply knowledge base for determining the web page relevance. The variations of classic focused crawler are semantic crawler and the social semantic focused crawler. The semantic focused crawler [8] is designed and implemented for crawling from www. The www has information overload for solving it, uses semantic focused crawler. This utilizes domain ontology for expanding the search topic and urls to start the crawler. Kozanidis [9], this paper utilizes ontology for focused crawler, which automatically builds the training set with relevant and non-relevant web pages. To download and search for the search topic the crawler identifies the web pages first and then download it based o our search topic. Zanardi et.al [10], this paper deals with ranking the related information. This finds the similarities between the relevant information retrieved from the SBS. After finding the relevance resources from the SBS rank the relevance resources by querying the search topic. Trojahn et.al [11], this paper defines the approaches for composite ontology mapping. The approach used in this paper is the cooperative approach. This extends the ontology with their classification. Here, they apply different mapping algorithm and for changing the results use cooperate solution. This gives the better results than the individual ontology. Choi et.al [12], it explained the enabling interoperability across the systems and applications. In this, for combining the distributed and the heterogeneous system requires the mapping technique. This provides the understanding the ontology mapping, the classification of it and their tools and systems. The mapping may be in any one of these forms. They are mapping between the global and local ontology, mapping between the local ontologies and mapping in ontology merge and the alignment. III. MAPPING ALGORITHM FOR RETRIEVING RELEVANT INFORMATION The focused crawler is to find the relevant information from the web based on the given search topic. This retrieves the content by using the keyword. While searching the content it follows the keyword based search. In this first it extracts the content from the web resources. By extracting the content itself we store the preferences in form of bookmarked data. From the bookmarked pages contains both the relevant information as well as the irrelevant information. Extracting the relevant information from the bookmarking content is the major challenge, here it uses focused crawler for retrieving the tagged resources. The search pattern is used by the crawler for searching the web pages of the interest in the social site. The crawler makes use of the ontology for finding the seed selection process. By using the seed selection criteria, we initialize the queue with the urls. From that url we select one url for further processing and fetches the content of that url. After fetching the content, parse the pages using the page relevance criteria. In this page relevance criterion, finds the page count and the word count where the relevant information are available for the search topic. After checking the page relevance, the contents are added to the priority queue, the relevant page url are enqueued based on the page relevance in the url, this is done by using the page priority criteria.

WEB DATABASE

1

0

Data Selection & Retrieval

Extracting Data

4

6 Enumerate

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3 Ontology Based Content Retrieval

Basic Average, Advanced Content

Ontology Detection

Parsing & Page Priority

Tagged Resource

Seed Selection

5

2

2.1

2.1.1

7 Mapping

View Result

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 287- 293

Fig 2 Data flow diagram for focused crawler

Fig 2 shows the data flow diagram for the focused crawler. In this the crawler first extracts the data from the wed database. After extracting the data from the web databases, the next step is to select the data and the retrieving the data selected data. Here, the crawler first initiates the processing for selecting the data. After extracting the content we store the relevant information url as the tagged resources. From the tagged resources the url are selected this are all done by the page priority. Then next step is to find the best, average and advanced content in the selected information. After detecting the information, we enumerate the url for processing and we find the mapping between the information. Then find the page count and the word count of the selected url are governed by the page relevance. This evaluates the relevance of the hypertext document. All the parsed URls are enqueued in priority queue, if the page is considered as non-relevant it doesn’t add in queue. The mapping algorithm is used to retrieve the relevant information. This process continues till it attains the termination condition.

IV. EXPERIMENTAL RESULT In this paper, the crawler first fetches the web pages from the web database. The search engine starts crawling based on the given search topic. The results of the search topic are saved as bookmarks, which contains both the relevant information and also the irrelevant information. From the bookmarked pages, we are extracting the needed information easily. Selecting the url for searching the information in that links. The web resources are retrieved, after retrieving the content starts parsing. Fig 3 shows the parsing of the content. While parsing the content, it splits the content into three divisions they are super content which contains the more relevant information, related content which contains the somewhat related content and sub content which gives only the sub information.

Fig 3 parsing the web resources

After parsing the content, we enumerate the url for further processing. First select the url from the seed url by seed selection criterion and proceed to attain relevant information.fig 4 shows the selecting of url for processing. After selecting the url and retrieving the web resources for that selected url. Then use the mapping algorithm for finding the relevant information from those resources.

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 287- 293

Fig 4 selecting the url for processing

Fig 5 shows the mapping relevance. In this mapping algorithm is used to find the relevance between the pages. To find mapping resources we must give the starting and the ending data for finding the exact mapping information. if we give the starting and ending data from the available resources it extracts the more relevant information between those keywords. The number of page occurrences are in that url are also found.

Fig 5 finding the mapping relevance

Fig 6 shows the analyses of the page and word count and also the semantic similarity between the content. The page and word count are analysed by retrieving the relevant information based on the keyword. These information are collected from the content retrieved. By using the relevant information, analyses the similarity between the resources. It finds the relevance between the tagged resources by using the semantic retrieval; it retrieves only the relevant information by using the keyword.

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Fig 6 analysis of page and word count and semantic similarity between the information

Fig 7 shows the analyses of the mapping relevance. The mapping relevance’s between the content are found from the relevant information. By using the mapping algorithm, easily finds the relevant information and also attains the available resources more easily based on the search topic.

Fig 7 Analysis of mapping relevance

V. CONCLUSION This paper uses the social information and semantic information for retrieving the relevant content from the web. The focused crawler is used for finding the relevant information based on the search topic. This uses the concepts ontology for expanding the quest topic and also for determining the semantic relevance between the search topics. By using the keyword, the concepts are structured semantically. The mapping algorithms were proposed in this paper. This is to improve the accuracy of retrieving the relevant information. The performance also gets increased by using the mapping algorithm. The word count and page count are analyzed. The page relevance, mapping relevance and the semantic similarity between the pages were analyzed and yields the better results based on keyword by using mapping algorithm. REFERENCES [1] [2]

Punam Bedi, Anjali Thukral, Hema Banati Focused crawling of tagged web resources using ontology, Computers and Electrical Engineering 39 (2013) pp 613–628. Ramya.R.S, Raja Ranganathan.S, Dr. S.Karthik A Brief Survey on Improving the Efficiency of Revisiting Concepts in Semantic web ISSN: 2278 – 7798 International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 1, January 2013.

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[3]

Chakrabarti S, van den Berg M, Dom B. Focused crawling: a new approach to topic-specific web resource discovery. Comput Netw 1999;31(11–16):1623–40. [4] C. Saranya, R.S. Ramya, Crawling of tagged web resources using alignment algorithm in ontology, international conference on advances in information technology, ISBN 978-1-941505-01-4@ 2014. [5] Soner Kara, O¨zgur Alan, Orkunt Sabuncu, Samet Akpınar, Nihan K.Cicekli, Ferda N.Alpaslan, “An ontology based retrieval system using semantic indexing”, Elsevier information Systems 37 (2012) 294–305. [6] Hong-Hai Do, Erhard Rahm, “COMA - A system for flexible combination of schema matching approaches”, Proceedings of the 28th international conference on Very Large Data Bases, page 610--621. Hong Kong, China, VLDB Endowment, (2002). [7] Thukral A, Mendiratta V, Behl A, Banati H, Bedi P. FCHC: a social semantic focused crawler. In: Int conference on advances in computing and communications. Part II. CCIS, vol. 191; 2011. p. 273–83. [8] Bedi P, Thukral A, Banati H, Behl A, Mendiratta V. A multithreaded semantic focused crawler. J Comput Sci Technol, in press [Springer]. [9] Kozanidis L. An ontology-based focused crawler. In: LNCS 5039. Springer; 2008. p. 376–9. [10] Zanardi V, Capra L. Social ranking: uncovering relevant content using tag-based recommender systems. In: RecSys ‘08 ACM conference on recommender systems; 2008. p. 51–8. [11] Cassia Trojan, Marcia Moraes, Paulo Quaresma and Renata Vieira, A Cooperative approach for Composite Ontology Mapping, Journal on data semantics vol 4900, pp 237-263, 2008. [12] Namyoun Choi, Yeol Song, Hyoil Han, A Survey on ontology mapping, SIGMOD record, vol 35, no.3, Sep 2006.

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Crawling of Tagged Web Resources Using Mapping ...

Keywords— Focused crawler, ontology, ontology-matching, mapping algorithm, RDF ... Ontology matching is the challenging factor in otology which specifies the entities of needed information in domain. .... [6] Hong-Hai Do, Erhard Rahm, “COMA - A system for flexible combination of schema matching approaches”,.

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