Personalized Semantic Search Engine

EasyChair Preprint no. 12543

Abstract

The Web 2.0, which many folks use nowadays, serves as a vast collection of interconnected documents that gets transferred by computers and keeps being shown to individuals.

A search engine is considered one of the most important tools to discover any information from WWW. In spite of having lots of development and novel research in current search engines techniques, they are still syntactic in nature and display search results on the basis of keyword matching without understanding the meaning of the query, resulting in the production of a list of Webpages containing a large number of irrelevant, and sometimes even unreliable documents as an output.

Simply, most of the current-day search engines are keyword-based search engines, meaning that they focus on each keyword of the search query they’re fed with. Now, this methodology may seem ideal but most of the time, the search results of these search engines are irrelevant. And even the Web is mostly unstructured which makes it difficult to return a proper search result. Semantic Web (Web 3.0), the next version of the World Wide Web is being developed with the aim of mitigating the problems faced in Web 2.0 by representing data in a structured form, and for discovering such data from Semantic Web, Semantic Search Engines (SSEs) are being developed in many domains.

This paper provides a survey on some of the prevalent SSEs focusing on their architecture; their working and techniques; a practical work on the performance of an SSE and a normal keyword-based search engine; and presents a comparative study on the basis of techniques that different SSEs follow.

Keyphrases: machine learning, Python, Semantic Search Engine

@Booklet{EasyChair:12543,
  author = {Bhanu Teja Sudalagunta and Manikanta Pinabakala and Hari Durga Kode and Damini Polamuri},
  title = {Personalized Semantic Search Engine},
  howpublished = {EasyChair Preprint no. 12543},

  year = {EasyChair, 2024}}