Using RDF Summary Graph For Keyword-based Semantic Searches

TL;DR

This paper introduces a semantic search framework for RDF data that enhances keyword-based searches by leveraging entity type semantics and proximity, providing relevant results with confidence scores.

Contribution

It presents a novel RDF summary graph-based framework that improves keyword search efficiency and result relevance using near neighbor exploration and semantic relatedness scoring.

Findings

Effective retrieval of semantically related RDF resources.

High accuracy in relevance confidence scoring.

Improved search efficiency on RDF datasets.

Abstract

The Semantic Web began to emerge as its standards and technologies developed rapidly in the recent years. The continuing development of Semantic Web technologies has facilitated publishing explicit semantics with data on the Web in RDF data model. This study proposes a semantic search framework to support efficient keyword-based semantic search on RDF data utilizing near neighbor explorations. The framework augments the search results with the resources in close proximity by utilizing the entity type semantics. Along with the search results, the system generates a relevance confidence score measuring the inferred semantic relatedness of returned entities based on the degree of similarity. Furthermore, the evaluations assessing the effectiveness of the framework and the accuracy of the results are presented.