Presentation an Approach for Optimization of Semantic Web Language Based on the Document Structure

TL;DR

This paper introduces a novel algorithm for optimizing semantic web language queries by utilizing document structure and enquiry schemas to avoid blind search of dataset points.

Contribution

The paper presents a new algorithm that improves semantic web query efficiency by pre-processing enquiry schemas to guide targeted dataset searching.

Findings

Reduces the need for blind search in dataset querying

Enhances query processing efficiency in semantic web applications

Provides a structured approach to locate enquiry answers more directly

Abstract

Pattern tree are based on integrated rules which are equal to a combination of some points connected to each other in a hierarchical structure, called Enquiry Hierarchical (EH). The main operation in pattern enquiry seeking is to locate the steps that match the given EH in the dataset. A point of algorithms has offered for EH matching; but the majority of this algorithms seeks all of the enquiry steps to access all EHs in the dataset. A few algorithms such as seek only steps that satisfy end points of EH. All of above algorithms are trying to locate a way just for investigating direct testing of steps and to locate the answer of enquiry, directly via these points. In this paper, we describe a novel algorithm to locate the answer of enquiry without access to real point of the dataset blindly. In this algorithm, first, the enquiry will be executed on enquiry schema and this leads to a schema. Using this plan, it will be clear how to seek end steps and how to achieve enquiry dataset, before seeking of the dataset steps. Therefore, none of dataset steps will be seek blindly.