# Semantic Web Prefetching Using Semantic Relatedness between Web pages

## TL;DR

This paper proposes client-side algorithms to determine semantic relationships between web pages using keywords and anchor texts, aiming to improve web prefetching without additional server or designer load.

## Contribution

It introduces algorithms for semantic relation detection that operate on the client side, reducing server and designer overhead in semantic web prefetching.

## Key findings

- Algorithms effectively identify semantic relations between pages.
- Client-side implementation reduces server load and designer effort.
- Potential to improve web prefetching accuracy and efficiency.

## Abstract

Internet as become the way of life in the fast growing digital life.Even with the increase in the internet speed, higher latency time is still a challenge. To reduce latency, caching and pre fetching techniques can be used. However, caching fails for dynamic websites which keeps on changing rapidly. Another technique is web prefetching, which prefetches the web pages that the user is likely to request for in the future. Semantic web prefetching makes use of keywords and descriptive texts like anchor text, titles, text surrounding anchor text of the present web pages for predicting users future requests. Semantic information is embedded within the web pages during their designing for the purpose of reflecting the relationship between the web pages. The client can fetch this information from the server. However, this technique involves load on web designers for adding external tags and on server for providing this information along with the desired page, which is not desirable. This paper is an effort to find the semantic relation between web pages using the keywords provided by the user and the anchor texts of the hyperlinks on the present web page.It provides algorithms for sequential and similar semantic relations. These algorithms will be implemented on the client side which will not cause overhead on designers and load on server for semantic information.

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Source: https://tomesphere.com/paper/1706.09206