A survey of RDF stores &amp; SPARQL engines for querying knowledge graphs

Ali, Waqas; Saleem, Muhammad; Yao, Bin; Hogan, Aidan; Ngomo, Axel-Cyrille Ngonga

doi:10.1007/s00778-021-00711-3

Cited by 74 publications

(22 citation statements)

References 169 publications

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“…There are two key points for complex join queries: join ordering and selecting the most suitable join algorithm [29]. Algorithm 1 shows a detailed illustration of our SPARQL join query algorithm for semantic search.…”

Section: Sparql Join Query Algorithms For Learned Semantic Indexmentioning

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: Sparql Join Query Algorithms For Learned Semantic Indexmentioning

confidence: 99%

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

2022

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“…Such an approach aims to learn from a set of examples a classification or regression function, making it possible to subsequently provide an assignment to one of the classes (corresponding to the modes of operation) for any newly provided measurement. We also speak of diagnosis by pattern recognition (Resource Description Framework (RDF)) [7] insofar as the processing developed will seek to recognize the "shape" of the observation in order to affect it then. The set of N examples used to learn this relation is called the set learning process, each example is made up of variables or descriptors generally with a value in ℝ 𝑃 describing the measurement signals.…”

Section: Introductionmentioning

confidence: 99%

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

Alaya

Sellami

2023

Preprint

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The supervised and semi-supervised learning framework does not always correspond to the situations encountered in vehicular networks. The labeling work is therefore often laborious and expensive. This is why the development of solutions to deal with imperfect labels was of particular interest to us during this research article. We introduce in this paper the formulation of the classification problem when the information available on the labels of the examples used for learning is imperfect. We also present an efficient way to solve classification problems, even when some of the labels provided to learn the classification function are wrong. Furthermore, we present our work on the extension of statistical learning methods in the environment of vehicular networks. To build an expectation-maximization (EM) algorithm capable of optimizing the marginal log-likelihood of the observed data, the path we will take follows the classic approach encountered in the probabilistic framework. Different experiments were carried out to analyze the behavior of our algorithm when “soft” labels in vehicles are used. These experiences have allowed us in particular to highlight the contribution of "soft" labels in this context to represent information on the reliability of the labels and thus significantly improve the performance of vehicular networks.

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“…How such a graph is stored on disk, can change between database management systems. An example of a more specific graph database model are, triple stores, which store everything as an edge, including properties, while other graph database engines store data in different manners, for example as a multigraph or adjacency list [12] , [13] However this review will not cover this topic in more detail, and while there are performance difference to be observed between different data management and storage options for specific use cases, this topic is of lower relevance for users wishing to use a ready-made solution, i.e. a database management system.…”

Section: Introductionmentioning

confidence: 99%

“…a database management system. More information on this topic can be found in these reviews [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Pavel

Saarimäki

Möbus

et al. 2022

Computational and Structural Biotechnology Journal

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A survey of RDF stores & SPARQL engines for querying knowledge graphs

Cited by 74 publications

References 169 publications

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

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

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

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