Social Semantic Retrieval and Ranking of eResources

Abstract

World Wide Web provides a huge collection of learning resources. However, as traditional retrieval algorithms lack the use of semantics to retrieve relevant documents, voluminous information is retrieved most of which may be irrelevant to the posted query. Due to which the learning process of a learner is slowed down. Hence, a need is felt to develop a retrieval and ranking method that produces semantically relevant web resources with information need. For this reason, the paper proposes semantically relevant retrieval and ranking of web resources that uses top N resource links returned from a search engine as seed, domain ontologies to compute semantic relevance, and data from Social Bookmarking System (SBS) to retrieve additional semantically relevant resources. Finally all retrieved resources are ranked according to the query relevancy using Vector Space Model (VSM). The proposed approach presented in this paper is elucidated in three parts: (i) a method that expands a posted query using semantic relevance by using ontologies, (ii) an algorithm to retrieve semantically relevant web resources by simulating human cognition using SBS, and (iii) a new approach to compute social semantic ranking of retrieved web resources. Thus it utilizes collective advantages of Social Bookmark Tagging System and Semantic technologies. Improvement in results obtained by the proposed approach in contrast to the existing results retrieved by search engine is apparent from empirical evaluation.

A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Systems

“…Bedi et al [56], [57] proposed SR3 that exploits advantages of social bookmarking services and Semantic Ontologies. Query is expended by using domain ontologies, where query-tag Qo becomes Q = {Q 0 , Q p , Q c }.…”

Architecture to Organize Social Semantic Relevant Web Resources in a Knowledgebase

“…The site also allows users to share these bookmarks. Taking advantage of this information as feedback for web resources, their associated bookmarks are used to determine document relevance for a concept through an approach called Focused Crawling using Human Cognition (FCHC) [9]. These retrieved annotations and associated web resources are then applied with pre existing domain ontologies to compute social semantic similarity between the topic and the web resources.…”

“…The semantic resource retrieval and their social semantic ranks, relevant to each of the domain concept are computed using an algorithm called Social Semantic Relevant Resource Retrieval and Ranking (SSR4) [9]. Thus retrieval procedures and computation for relevancy are completed offline before a learner submits her/his query topic.…”

“…These U etrieved from S esource in turn ntology. Usin nstances of co oncepts in dom ore additiona esource to a co A concept a nked with eac as a specific omain ontolo epresenting a ndividual in a omain ontolo elevance value erm (original lgorithm [9]. ontent fragme hat resource ( he example in nd fro between URL link conc …”

“…2) Social semantic focused crawler A social semantic focused crawler FCHC (Focused Crawling using Human Cognition) [9] has been used to retrieve relevant resources. The pattern used to search relevant resources in social bookmarking site (SBS) makes this crawler different from others.…”