A Novel Architecture of Ontology-based Semantic Web Crawler

Rana, Ram Kumar; Tyagi, Nidhi

doi:10.5120/6365-8724

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…The literature survey reveals there have been few significant attempts to merge information retrieval and ontological models, [5] proposed a text processing system to build ontological domain. In the research paper [6], an architecture has been proposed for ontology based semantic web crawler that can exploit the semantic metadata to efficiently discover and extract information from the Semantic Web and the Semantic matching between content of downloaded web page and ontology is used to guide the crawler towards relevant information. The systems with context models, are discussed in [7], represents context in form of attribute-value tuples.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Contextual Relevance of the Web Document using F-P Growth

Tyagi¹,

Rishi²,

Agarwal³

2013

IJCA

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The crawled web pages should be organized in a fashion where they are more understandable to machine, for producing the results which are meaningful and relevant. The set of web pages can be categorized into different contextual sense if the crawler has the technique to understand their meaning and the domain identification. The contextual relevance of the web documents can be known, if the frequent occurring patterns of the keywords in the web page are identified. This can be achieved through data mining technique for generating frequent patterns, using FP-Growth. It will help in deducing the set of keywords of the documents and this knowledge is added in the knowledge store which will further facilitate in the building the ontology for the crawled web pages and organizing them and thus increasing the rank of the document.

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Section: Related Workmentioning

confidence: 99%

Extraction of Contextual Relevance of the Web Document using F-P Growth

Tyagi¹,

Rishi²,

Agarwal³

2013

IJCA

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“…A crawler [7] or bot or spider is a program follow URLs and traverse the web in order to download web pages through multi-threaded downloader. A multi-thread crawler generate more than one thread at a simultaneously those request web pages through http request.…”

Section: Multi-threaded Downloadermentioning

confidence: 99%

“…There are many data structure like Hash table and different types of trees like Rtree, B-tree to represent this. An example is shown how this index store terms with their context for term mouse [7].…”

Section: Index Servermentioning

confidence: 99%

Ontology based Semantic Indexing Approach for Information Retrieval System

Kumar¹,

Rana²,

Singh³

2012

IJCA

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This paper shows how the gap between the texts based web pages and the Resource Descriptive Framework based pages of the semantic web can be bridged by ontologies. Most traditional search engines use indexes that are engineered at the syntactical level and come back hits based mostly on straightforward string comparisons or use the static keyword based indexing. However, the indexes don't contain synonyms, cannot differentiate between homonyms ("mouse" as a Pointing device vs. "Mouse" as a living animal) and users receive completely different search results after they use different conjugation varieties of identical word. During this work, we have a tendency to gift a system that uses ontologies and Natural Language Processing techniques to construct index, and therefore supports word sense disambiguation. Therefore the retrieval of document that contains equivalent term as the context demands is achieved to provide efficient search engines through ontological indexing. General TermsSemantic indexing theory, Architecture, Algorithm.

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“…Generally, the World Wide Web (WWW) is a collection of hypertext documents that are connected together [1,2]. The WWW presents an architectural design for gaining access to documents linked that are widespread across millions of computers on the Internet [3][4][5]. Obtaining valuable information from the immense volume of information on the internet was one of the most difficult undertakings.…”

Section: Introductionmentioning

confidence: 99%

Web Pages Ranking Algorithms: A Survey

Abdulrahman

2021

QAJ

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Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.

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A Novel Architecture of Ontology-based Semantic Web Crawler

Cited by 4 publications

References 14 publications

Extraction of Contextual Relevance of the Web Document using F-P Growth

Extraction of Contextual Relevance of the Web Document using F-P Growth

Ontology based Semantic Indexing Approach for Information Retrieval System

Web Pages Ranking Algorithms: A Survey

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