Tangina Sultana  Young-Koo Lee

10.5626/JOK.2022.49.3.241

compression encoding syntactic encoding knowledge bases

Due to the enormous growth of entity-centric search and natural language-based queries, the applicability of Knowledge Bases (KBs) is increasing exponentially. Therefore, it requires efficient SPARQL queries. Resource Description Framework (RDF) engines mostly employ order, coordinates, syntactic, and hash-based encoding for managing KBs. However, most current schemes do not have a better compression ratio, faster loading time, or efficient query performance. To address these concerns, in this paper, we propose a novel approach for detecting frequent and semantically related terms to achieve a higher compression ratio and enhance the performance of SPARQL queries on compressed and encoded data. This scheme was based on a dictionary encoding algorithm, a combined approach of statistical and semantic schemes. We also introduced another scheme for identifying infrequent terms based on their semantics. The system then assembled semantically related data into ontological classes that could further reduce the required memory footprint as well as loading time. We analyzed and compared the performance of our proposed scheme with those of existing state-of-the-art approaches. The simulation result affirmed that our proposed approach compressed and encoded KBs substantially better than existing systems.