Optimizing Keyword Search Over Federated RDF Systems

Li, Mingdao; Peng, Peng; Tian, Zhen; Qin, Zheng; Liu, Yi

doi:10.1109/tbdata.2022.3224749

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…The proposed algorithm can be applied to top-k queries for single variable ordering as well as expression ordering [17]. Li et al [18] perform a mapping of keywords to draw RDF graphs over federated queries in the proposed model. The utilization of a full-text search has been done to map keywords to schema graphs which has been further used to generate SPARQL queries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The utilization of a full-text search has been done to map keywords to schema graphs which has been further used to generate SPARQL queries. Further, to optimize the query evaluation process, the technique of query rewriting has been proposed [18]. Peng et al [19] present a study on a federated RDF system with optimization multi-query evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The running times shown in Tables 6, 7, and 8 are in milliseconds and represent the AET (average execution time). They can also be visualized using Figures 16,17,and 18. It has been determined that the SPARQL query is a fundamental element for managing various data kinds on the semantic web for various objectives.…”

Section: Comparative Study With Benchmark Sparql Queries With Hsoamentioning

confidence: 99%

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SPARQL Optimization Using Re-ordering Joining Patterns with Surrogate Key Concept and Subset Patterns

Gupta,

Malik

2024

JWE

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Semantic web data resides on the web in the form of knowledge graphs known as RDF graphs and searching around the web has been always a crucial task. For the data retrieval of RDF data of the semantic web, SPARQL query language has been used which in turn is based on triple patterns and joins. Optimization of SPARQL query has been a problematic concern for decades due to the large amount of triple patterns associated with RDF data. Although several researchers have put a lot of effort into the optimization of SPARQL query, it is difficult to understand the concept from scratch due to its diversified nature. This paper analyses various optimization techniques for the SPARQL query used with the semantic web to process knowledge graphs. These techniques include join-based, heuristic-based, rule-based, and indexing-based approaches for optimization. This paper will help researchers in this domain to easily get into the core concept of SPARQL execution along with various optimization approaches used for query processing, which can help in various other domains like linked open data and information retrieval. In this paper, an optimization algorithm HSOA (hybrid SPARQL optimization algorithm) has been proposed, which comprises the features of index-based, cost-based, and triple reordering-based optimization approaches. The proposed hybrid algorithm has been designed specifically for n-triple RDF data, which comprises subset patterns, and surrogate key concepts. The results produced by the proposed algorithm are encouraging and have also been tested and compared with the benchmark dataset and SPARQL queries like LUBM, BSBM, and SP2Bench.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Study With Benchmark Sparql Queries With Hsoamentioning

confidence: 99%

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