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Efficient Approach for Encoding and Compression of RDF Knowledge Bases
Tangina Sultana, Young-Koo Lee
http://doi.org/10.5626/JOK.2022.49.3.241
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.
Efficient Shortest Path Techniques on a Summarized Graph based on the Relationships
Hyunwook Kim, HoJin Seo, Young-Koo Lee
http://doi.org/10.5626/JOK.2017.44.7.710
As graphs are becoming increasingly large, the costs for storing and managing data are increasing continuously. Shortest path discovery over a large graph requires long running time due to frequent disk I/Os and high complexity of the graph data. Recently, graph summarization techniques have been studied, which reduce the size of graph data and disk I/Os by representing highly dense subgraphs as a single super-node. Decompressing should be minimized for efficient shortest path discovery over the summarized graph. In this paper, we analyze the decompression performance of a summarized graph and propose an approximate technique that discovers the shortest path quickly with a minimum error ratio. We also propose an exact technique that efficiently discovered the shortest path by exploiting an index built on paths containing super-nodes. In our experiments, we showed that the proposed technique based on the summarized graph can reduce the running time by up to 70% compared with the existing techniques performed on the original graph.
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