Survey of RDF Stores &amp; SPARQL Engines for Querying Knowledge Graphs

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

doi:10.36227/techrxiv.14376884.v1

Cited by 9 publications

(3 citation statements)

References 180 publications

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“…Vertical partitioning is the typical configuration of the datasets used to build the nextgeneration VLEs. Databases used to store and query e-learning data can be implemented with diverse storage techniques, including graph databases [39], e.g., RDF (Resource Description Framework) triplestores, and relational databases [40].…”

Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

“…Despite its currently low popularity (0.4% ranking score), RDF triplestores are graph databases that enable sharing schemata through ontologies and vocabularies. Besides, RDF triplestores are usually implemented on top of relational DBMS or graph databases [40], making LDP an acceptably interoperable solution to publish and share different providers' data schemata, either relational or graph-based. Another choice would have been to use the GraphQL Schema Definition Language (SDL) (available at https://graphql.org/learn/ schema, accessed on 9 September 2021) to define both the providers' schema and the data mashup schema.…”

Section: Database Management Technologymentioning

confidence: 99%

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Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.

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Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

Section: Database Management Technologymentioning

confidence: 99%

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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“…In addition to graph-based queries, SPARQL, a query language for semantic web databases, is employed for querying and analyzing the knowledge graph. It offers powerful capabilities for retrieving specific data patterns and conducting complex analyses [15] .…”

Section: Knowledge Graph Construction and Queryingmentioning

confidence: 99%

Construction of a power quality and electrical equipment knowledge graph for equipment status assessment

Liu,

Zhang,

Zhang

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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In modern power systems, the condition of electrical equipment is vital for maintaining power grid stability and efficiency. This paper presents the construction of a comprehensive knowledge graph for power quality and electrical equipment, derived from Chinese web sources. This knowledge graph facilitates real-time monitoring, predictive maintenance, and informed decision-making for electrical equipment management. The study covers core technologies, including data acquisition through web crawling using Selenium, entity recognition using BiLSTM-CRF, and relation extraction using R-BERT. The practical architecture includes ontology design, data pre-processing, entity recognition, relation extraction, and knowledge graph construction. SPARQL queries enable complex analysis, and applications support power quality monitoring and equipment health assessment. The knowledge graph serves as a foundation for understanding power quality and equipment status in a Chinese context, emphasizing the relationship between power quality and equipment failure. Ongoing efforts will expand and refine the graph to enhance its value for power qualitybased equipment status assessment.

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Scalable Transformation of Big Geospatial Data into Linked Data

Mandilaras

Koubarakis

2021

The Semantic Web – ISWC 2021

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Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Cited by 9 publications

References 180 publications

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Construction of a power quality and electrical equipment knowledge graph for equipment status assessment

Scalable Transformation of Big Geospatial Data into Linked Data

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