Using RDF Summary Graph For Keyword-based Semantic Searches
This work addresses the need for improved semantic search capabilities in the Semantic Web domain, but it appears incremental as it builds on existing RDF and keyword-based search methods.
The study tackled the problem of efficient keyword-based semantic search on RDF data by proposing a framework that uses near neighbor explorations and entity type semantics to augment results with closely related resources, and it presented evaluations showing effectiveness and accuracy.
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.