A Two-Phase Method for Optimization of the SPARQL Query

Lin, Xiaoqing; Dong-yang, Jiang

doi:10.1155/2022/4624856

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…This approach requires knowledge of SPARQL execution plans and the shapes used to filter the data. The SPARQL graphs can be explored using joining patterns of triples used with queries [38]. The execution plan of the SPARQL queries can also be analyzed to find the sequence of the operations used for information retrieval.…”

Section: Query-based Approachmentioning

confidence: 99%

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: Query-based Approachmentioning

confidence: 99%

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

Gupta,

Malik

2024

JWE

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“…One of the difficulties of dealing with a RDF data set is foreseeing execution of SPARQL questions before they are executed (Zhang et al, 2018). With a fast development in the accessible asset portrayal system (RDF) information from unique spaces, the SPARQL question handling with chart structures has become progressively significant (Lin et al, 2022). In this pursuit, a two-stage SPARQL question streamlining technique to deal with the SPARQL question was proposed in literature.…”

Section: Introductionmentioning

confidence: 99%

Machine learning classifiers to predict the quality of semantic web queries

Ramalingam,

Kumar

et al. 2024

The Scientific Temper

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In this research, a classification framework to automatically identify well and poorly designed SPARQL queries is proposed. Evaluating SPARQL queries becomes a difficult challenging issue because of the query design and the volume of data to be handled. The proposed context applies various machine learning algorithms including decision trees, k nearest neighbours, support vector machine, and naive Bayes. In addition, two different feature extraction techniques called TFIDF measure and count vectorizer are measured to identify the key features. The experimental results show that the four machine learning classifiers applied are able to classify the SPARQL queries into three categories like well, accepted, and poorly designed queries. It also provides hopeful results with respect to recall, precision, and F1-score. In datasets used for experimentation, it was found that the decision trees classifier outperforms well compared to other classifiers by achieving 92% in terms of F1-measure. Also, the count vectorizer performs well in measuring the TFIDF property to predict the poorly designed queries.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

Retracted: A Two-Phase Method for Optimization of the SPARQL Query

Sensors

2024

Journal of Sensors

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A Two-Phase Method for Optimization of the SPARQL Query

Cited by 4 publications

References 29 publications

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

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

Machine learning classifiers to predict the quality of semantic web queries

Retracted: A Two-Phase Method for Optimization of the SPARQL Query

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