Extraction of the contents in the web texts by content-density distribution

Kitahara, Saori; Tamura, Koya; Hatano, Kenji

doi:10.1504/ijkesdp.2011.045723

Cited by 1 publication

(1 citation statement)

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“…The main significance is on web analytics. Kitahara et al (2011) have proposed a unique strategy of content density approach for recommending webpage contents. The method grasps the webpage and further focuses on extracting the portion of the contents that is relevant to the query from the webpage.…”

Section: Related Workmentioning

confidence: 99%

An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Deepak

Ahmed²,

Skanda

2019

IJISTA

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Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.

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Section: Related Workmentioning

confidence: 99%