An empirical study on the evaluation of the RDF storage systems

Mahria, Bilal Ben; Chaker, Ilham; Zahi, Azeddine

doi:10.1186/s40537-021-00486-y

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…We should consider the issue of how to select the most suitable strategy for storing RDF data. For this, we investigated the balancing between two performance metrics, i.e., query response time and index load time [30]. The objective of the experiments was to show that we could achieve excellent join query performance with retrieval accuracy.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques

2022

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Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.

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Section: Experimental Evaluationmentioning

confidence: 99%

Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques

2022

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“…The total number of product entities is 478 626, and the total number of user entities is 1 000 000. User comments and opinion knowledge are simulation data (Mahria, B. B., Chaker, I., & Zahi, A.…”

Section: Variable Combination Connection Strategymentioning

confidence: 99%

Large-Scale Commodity Knowledge Organization and Intelligent Query Optimization

2022

International Journal of Mobile Computing and Multimedia Communications

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The Internet is facing the era of knowledge interconnection Web3.0, and its goal is to realize a more intelligent network that can be understood by both humans and machines. In this environment, various types of knowledge graphs have emerged. Because of the heterogeneity of knowledge, commodity knowledge makes its management more challenging. A large-scale product knowledge organization framework is designed, objective product classification knowledge is combined with subjective user perspectives in the framework, a neural network-based learning index technology is proposed to improve query efficiency. According to the properties of the knowledge structure and the characteristics of query requirements, a connection strategy is realized based on sub-variable combination.The experimental results show that, compared with the existing knowledge management system, the proposed method has a significant improvement in the retrieval efficiency of large-scale commodity knowledge.

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