Ontology based Semantic Indexing Approach for Information Retrieval System

Abstract

This paper shows how the gap between the texts based web pages and the Resource Descriptive Framework based pages of the semantic web can be bridged by ontologies. Most traditional search engines use indexes that are engineered at the syntactical level and come back hits based mostly on straightforward string comparisons or use the static keyword based indexing. However, the indexes don't contain synonyms, cannot differentiate between homonyms ("mouse" as a Pointing device vs. "Mouse" as a living animal) and users receive completely different search results after they use different conjugation varieties of identical word. During this work, we have a tendency to gift a system that uses ontologies and Natural Language Processing techniques to construct index, and therefore supports word sense disambiguation. Therefore the retrieval of document that contains equivalent term as the context demands is achieved to provide efficient search engines through ontological indexing. General TermsSemantic indexing theory, Architecture, Algorithm.

Modification of Keyword Selection Process to Get Least List with Weighted Keywords by using Essence of both ‘Baxendale’ and ‘Swanson’ Experiment

“…It defines as a shared understanding of some domain of interest. It acts by parsing the text from very basic to very advanced using different natural language processing technique [8,9].…”

Semantic Indexing and Concept Based Methods for Ontology Based Information Retrieval using Dbpedia

“…Redone frameworks [8] are favored looked the manual ones considering the exertion spent on the space. The frameworks in [2][3][4] use hand-made measures to evacuate data. Handmade measures are also utilized in semantic comment [9].…”

SemIndex: Semantic-Aware Inverted Index

“…An approach in [26] extends the inverted index structure by adding additional pointers linking each entry of the index to semantically related terms, (term, docIDs, relatedTerms). Yet, the authors in [12,26] do not provide the details on how concepts are selected from WordNet and how they are associated to each term in the index.…”

“…The first approach consists in adding additional entries in the index structure to designate semantic information. Here, the authors in [12] suggest extending the traditional (term, docIDs) inverted index toward a (term, context, docIDs) structure where contexts designate synsets extracted from WordNet, associated to each term in the index taking into account the statistical occurrences of concepts in Web document [1]. An approach in [26] extends the inverted index structure by adding additional pointers linking each entry of the index to semantically related terms, (term, docIDs, relatedTerms).…”