A Novel Approach for Semantic Prefetching Using Semantic Information and Semantic Association

Setia, Sonia; Kumari, Jyoti; Duhan, Neelam

doi:10.1007/978-981-10-6620-7_45

Cited by 3 publications

(5 citation statements)

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“…For the WUM method in our trials, we employed the Markov model-based technique [25] and the association rule mining technique [26]. For the WCM technique, we used the keyword-based approach [26]. As can be seen in Figures 7 and 8, the performance of the suggested model was superior to that of the other two models.…”

Section: Comparison Between Prefetching Systems Based On Wum Wcm and ...mentioning

confidence: 98%

“…There was a test cases series that was executed for a variety of sessions, ranging from shorter ones to longer ones. For the WUM method in our trials, we employed the Markov model-based technique [25] and the association rule mining technique [26]. For the WCM technique, we used the keyword-based approach [26].…”

Section: Comparison Between Prefetching Systems Based On Wum Wcm and ...mentioning

confidence: 99%

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Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Setia,

Jyoti,

Duhan

et al. 2024

JWE

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In intelligent web systems [2], web prefetching [27] plays a crucial role. In order to make accurate predictions for web prefetching, it is important but challenging to uncover valuable information from web use statistics [16]. Using statistics and domain expertise, this study presents a new approach dubbed SPUDK for efficient prefetching. In this paper, it is shown how web access logs can be used efficiently for browsing prediction. Our main focus is on the technique needed to manage the queries found in web access logs so that valuable information can be attained. We further process these access logs using a taxonomy and a thesaurus, WordNet, to find the semantics of queries. SPUDK, a system that organises use data into semantic clusters, is one example of this approach. Our contributions in this paper are as follows: (1) A technique to exploit query keywords from access logs. (2) An approach to enrich queries with semantic information. (3) A new similarity measure for finding similarity among URLs present in access logs. (4) A novel clustering technique to find semantic clusters of URLs. (5) Experimental evaluation of the proposed system. The proposed SPUDK system is evaluated using American Online (AOL) logs, which gives improvement of 39% in precision of prediction, 35% in hit ratio and reduction of 50.6% in latency on average as compared to other prediction techniques in the literature.

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Section: Comparison Between Prefetching Systems Based On Wum Wcm and ...mentioning

confidence: 98%

Section: Comparison Between Prefetching Systems Based On Wum Wcm and ...mentioning

confidence: 99%

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Setia,

Jyoti,

Duhan

et al. 2024

JWE

View full text Add to dashboard Cite

show abstract

“…e connections with higher chances were chosen for prefetching. Further, Setia et al [20] extended this work by considering the semantic preferences of the keywords present in the anchor text associated with the hyperlinks.…”

Section: Prefetching Techniques Using Content Miningmentioning

confidence: 99%

“…A series of test cases were run for several types of sessions, i.e., smaller to longer sessions. In our experiments, association rule mining and Markov model-based technique [11] have been used for the WUM technique, and the keyword-based approach [20] has been used for WCM. e proposed model performed well compared to the other two, as shown in Figure 16.…”

Section: Comparison Between Web Usage Mining Web Contentmentioning

confidence: 99%

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HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

Setia

Verma

Duhan

2020

Scientific Programming

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The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

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A hybridized semantic trust-based framework for personalized web page recommendation

Deepak

Shwetha

et al. 2018

International Journal of Computers and Applications

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A Novel Approach for Semantic Prefetching Using Semantic Information and Semantic Association

Cited by 3 publications

References 6 publications

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

A hybridized semantic trust-based framework for personalized web page recommendation

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