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Journal articles on the topic 'SPARQL'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Liu, Yunxing. "A Fuzzy Query Method Oriented to Knowledge Graph." Journal of Physics: Conference Series 2221, no. 1 (2022): 012051. http://dx.doi.org/10.1088/1742-6596/2221/1/012051.

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Abstract With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the

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Bizer, Christian, and Andreas Schultz. "The Berlin SPARQL Benchmark." International Journal on Semantic Web and Information Systems 5, no. 2 (2009): 1–24. http://dx.doi.org/10.4018/jswis.2009040101.

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Kim, HyeongSik, Padmashree Ravindra, and Kemafor Anyanwu. "From SPARQL to MapReduce." Proceedings of the VLDB Endowment 4, no. 12 (2011): 1426–29. http://dx.doi.org/10.14778/3402755.3402787.

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Almendros-Jiménez, Jesús M., Antonio Becerra-Terón, Ginés Moreno, and José A. Riaza. "Tuning fuzzy SPARQL queries." International Journal of Approximate Reasoning 170 (July 2024): 109209. http://dx.doi.org/10.1016/j.ijar.2024.109209.

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Zhang, Xiaowang, Jan Van den Bussche, Kewen Wang, Heng Zhang, Xuanxing Yang, and Zhiyong Feng. "On the Expressivity of ASK Queries in SPARQL." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 3057–64. http://dx.doi.org/10.1609/aaai.v34i03.5700.

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As a major query type in SPARQL, ASK queries are boolean queries and have found applications in several domains such as semantic SPARQL optimization. This paper is a first systematic study of the relative expressive power of various fragments of ASK queries in SPARQL. Among many new results, a surprising one is that the operator UNION is redundant for ASK queries. The results in this paper as a whole paint a rich picture for the expressivity of fragments of ASK queries with the four basic operators of SPARQL 1.0 possibly together with a negation. The work in this paper provides a guideline for

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Al Agha, I., and O. El-Radie. "Towards Verbalizing SPARQL Queries in Arabic." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 937–44. http://dx.doi.org/10.48084/etasr.630.

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With the wide spread of Open Linked Data and Semantic Web technologies, a larger amount of data has been published on the Web in the RDF and OWL formats. This data can be queried using SPARQL, the Semantic Web Query Language. SPARQL cannot be understood by ordinary users and is not directly accessible to humans, and thus they will not be able to check whether the retrieved answers truly correspond to the intended information need. Driven by this challenge, natural language generation from SPARQL data has recently attracted a considerable attention. However, most existing solutions to verbalize

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Al, Agha I., and O. El-Radie. "Towards Verbalizing SPARQL Queries in Arabic." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 937–44. https://doi.org/10.5281/zenodo.49884.

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With the wide spread of Open Linked Data and Semantic Web technologies, a larger amount of data has been published on the Web in the RDF and OWL formats. This data can be queried using SPARQL, the Semantic Web Query Language. SPARQL cannot be understood by ordinary users and is not directly accessible to humans, and thus they will not be able to check whether the retrieved answers truly correspond to the intended information need. Driven by this challenge, natural language generation from SPARQL data has recently attracted a considerable attention. However, most existing solutions to verbalize

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Ruchika, Gupta, and Gupta Rupal. "PROCESSING RESOURCE DESCRIPTION FRAMEWORK USING JENA ARQ AND VIRTUOSO." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 95–101. https://doi.org/10.5281/zenodo.10435420.

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<em>Semantic Web technologies have led to the development of RDF (Resource Description Framework) and OWL (Web Ontology Language) for the representation of data on the web. With the growing amount of RDF data, efficient processing and retrieval of information has become a major challenge. SPARQL (SPARQL Protocol and RDF Query Language) is the standard query language for RDF data, which has been widely used for querying RDF data. However, the performance of SPARQL queries can be significantly affected by the size of the RDF data and the complexity of the queries. In this paper, a set of SPARQL

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Florian, Thiery, Charlotte Schmidt Sophie, Homburg Timo, and Trognitz Martina. "The SPARQL Unicorn: An introduction." Squirrel Papers 2, no. 1 (2020): #1. https://doi.org/10.5281/zenodo.3742186.

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What ist the SPARQL Unicorn? How can a unicorn help researchers handling and working with Linked Open Data resources? This working paper will give an overview on the SPARQL unicorn history, the main idea, developed tools and projects.

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BARBIERI, DAVIDE FRANCESCO, DANIELE BRAGA, STEFANO CERI, EMANUELE DELLA VALLE, and MICHAEL GROSSNIKLAUS. "C-SPARQL: A CONTINUOUS QUERY LANGUAGE FOR RDF DATA STREAMS." International Journal of Semantic Computing 04, no. 01 (2010): 3–25. http://dx.doi.org/10.1142/s1793351x10000936.

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This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time. C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of exampl

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Li, Luye, Shuming Gao, Ying Liu, and Xiaolian Qin. "Enhanced SPARQL-based design rationale retrieval." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, no. 4 (2016): 406–23. http://dx.doi.org/10.1017/s089006041600038x.

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AbstractDesign rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two

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Jeon, MyungJoong, JinYoung Hong, and YoungTack Park. "SPARQL Query Processing System over Scalable Triple Data using SparkSQL Framework." Journal of KIISE 43, no. 4 (2016): 450–59. http://dx.doi.org/10.5626/jok.2016.43.4.450.

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Alkhateeb, Faisal, and Jerome Euzenat. "Constrained regular expressions for answering RDF-path queries modulo RDFS." International Journal of Web Information Systems 10, no. 1 (2014): 24–50. http://dx.doi.org/10.1108/ijwis-05-2013-0013.

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Purpose – The paper aims to discuss extensions of SPARQL that use regular expressions to navigate RDF graphs and may be used to answer queries considering RDFS semantics (in particular, nSPARQL and our proposal CPSPARQL). Design/methodology/approach – The paper is based upon a theoretical comparison of the expressiveness and complexity of both nSPARQL and the corresponding fragment of CPSPARQL, that we call cpSPARQL. Findings – The paper shows that nSPARQL and cpSPARQL (the fragment of CPSPARQL) have the same complexity through cpSPARQL, being a proper extension of SPARQL graph patterns, is mo

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Lorey, Johannes. "Identifying and determining SPARQL endpoint characteristics." International Journal of Web Information Systems 10, no. 3 (2014): 226–44. http://dx.doi.org/10.1108/ijwis-03-2014-0007.

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Purpose – The purpose of this study is to introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases. Design/methodology/approach – This study presents comprehensive approaches for deriv

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Shekarpour, Saeedeh, Sören Auer, Axel-Cyrille Ngonga Ngomo, Daniel Gerber, Sebastian Hellmann, and Claus Stadler. "Generating SPARQL queries using templates." Web Intelligence and Agent Systems: An International Journal 11, no. 3 (2013): 283–95. http://dx.doi.org/10.3233/wia-130275.

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Frosini, Riccardo, Andrea Calì, Alexandra Poulovassilis, and Peter T. Wood. "Flexible query processing for SPARQL." Semantic Web 8, no. 4 (2017): 533–63. http://dx.doi.org/10.3233/sw-150206.

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Vandenbussche, Pierre-Yves, Jürgen Umbrich, Luca Matteis, Aidan Hogan, and Carlos Buil-Aranda. "SPARQLES: Monitoring public SPARQL endpoints." Semantic Web 8, no. 6 (2017): 1049–65. http://dx.doi.org/10.3233/sw-170254.

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Kolas, Dave. "Supporting Spatial Semantics with SPARQL." Transactions in GIS 12 (December 2008): 5–18. http://dx.doi.org/10.1111/j.1467-9671.2008.01132.x.

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Shironoshita, E. P., Y. R. Jean-Mary, R. M. Bradley, and M. R. Kabuka. "semQA: SPARQL with Idempotent Disjunction." IEEE Transactions on Knowledge and Data Engineering 21, no. 3 (2009): 401–14. http://dx.doi.org/10.1109/tkde.2008.91.

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Pérez, Jorge, Marcelo Arenas, and Claudio Gutierrez. "Semantics and complexity of SPARQL." ACM Transactions on Database Systems 34, no. 3 (2009): 1–45. http://dx.doi.org/10.1145/1567274.1567278.

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Chekol, Melisachew Wudage, Jérôme Euzenat, Pierre Genevès, and Nabil Layaïda. "SPARQL Query Containment Under Schema." Journal on Data Semantics 7, no. 3 (2018): 133–54. http://dx.doi.org/10.1007/s13740-018-0087-1.

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Turova, I. A., and I. S. Postanogov. "Development of Intelligent SPARQL Query Editor." Vestnik NSU. Series: Information Technologies 19, no. 4 (2022): 85–95. http://dx.doi.org/10.25205/1818-7900-2021-19-4-85-95.

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The paper discusses the development of SPARQL query editor. This is an actual question because of the growth of Semantic Web data – the data presented in RDF/OWL formats. A comparative analysis of different types of editors and their main features is provided. In this paper, we propose a description of a SPARQL editor that combines three most useful features: intelligent completions, query visualization comparison of query results provided by different reasoners or without them. The editor provides SPARQL support as LSP service, this approach is considered a modern way to implement language su

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Hasnain, Ali, Qaiser Mehmood, Syeda Sana e Zainab, and Aidan Hogan. "SPORTAL." International Journal on Semantic Web and Information Systems 12, no. 3 (2016): 134–63. http://dx.doi.org/10.4018/ijswis.2016070105.

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Access to hundreds of knowledge bases has been made available on the Web through public SPARQL endpoints. Unfortunately, few endpoints publish descriptions of their content (e.g., using VoID). It is thus unclear how agents can learn about the content of a given SPARQL endpoint or, relatedly, find SPARQL endpoints with content relevant to their needs. In this paper, the authors investigate the feasibility of a system that gathers information about public SPARQL endpoints by querying them directly about their own content. With the advent of SPARQL 1.1 and features such as aggregates, it is now p

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Ma, Zongmin, Xiaoqing Lin, Li Yan, and Zhen Zhao. "RDF Keyword Search by Query Computation." Journal of Database Management 29, no. 4 (2018): 1–27. http://dx.doi.org/10.4018/jdm.2018100101.

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Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the R

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Lin, Xiaoqing, Fu Zhang, Danling Wang, and Jingwei Cheng. "RDF keyword search using multiple indexes." Filomat 32, no. 5 (2018): 1861–73. http://dx.doi.org/10.2298/fil1805861l.

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Since SPARQL has been the standard language for querying RDF data, keyword search based on keywords-to-SPARQL translation attracts more intention. However, existing keyword search based on keywords-to-SPARQL translation have limitations that the schema used for keyword-to-SPARQL translation is incomplete so that wrong or incomplete answers are returned and advantages of indexes are not fully taken. To address the issues, an inter-entity relationship summary (ER-summary) is constructed by distilling all the inter-entity relationships of RDF data graph. On ER-summary, we draw circles around each

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Michel, Franck, Catherine Zucker, Olivier Gargominy, and Fabien Gandon. "Integration of Web APIs and Linked Data Using SPARQL Micro-Services—Application to Biodiversity Use Cases." Information 9, no. 12 (2018): 310. http://dx.doi.org/10.3390/info9120310.

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In recent years, Web APIs have become a de facto standard for exchanging machinereadable data on the Web. Despite this success, however, they often fail in making resource descriptions interoperable due to the fact that they rely on proprietary vocabularies that lack formal semantics. The Linked Data principles similarly seek the massive publication of data on the Web, yet with the specific goal of ensuring semantic interoperability. Given their complementary goals, it is commonly admitted that cross-fertilization could stem from the automatic combination of Linked Data and Web APIs. Towards t

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Xinglei, X., M. Zhiliang, H. Huanmin, and X. Peng. "Automated extraction of attributes of IFC objects based on graph theory and SPARQL query." IOP Conference Series: Materials Science and Engineering 1218, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1757-899x/1218/1/012024.

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Abstract Building Information Modelling (BIM) has been widely adopted as an effective means for supporting information exchange in Architectural, Engineering and Construction (AEC) industry based on unified and platform-independent standards. Among them, the Industry Foundation Classes (IFC), an ISO standard for BIM, is the most widely used one. However, due to the complexity and flexibility of IFC, the extraction of the attributes of IFC objects is a challenging process for practitioners and software developers in AEC industry, which impedes in-depth application of BIM in many cases. To solve

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Fahrurrozi, Muhammad, and Azhari SN. "Rancang Bangun Plugin Protégé Menggunakan Ekspresi SPARQL-DL Dengan Masukan Bahasa Alami." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 11, no. 2 (2017): 155. http://dx.doi.org/10.22146/ijccs.18360.

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Semantic web is a technology that allows us to build a knowledge base or ontology for the information of the web page can be understood by computers. One software for building ontology-based semantic web is a protégé. Protege allows developers to develop an ontology with an expression of logic description. Protégé provides a plugin such as DL-Query and SPARQL-Query to display information that involve expression of class, property and individual in the ontology. The problem that then arises is DL-plugin Query only able to process the rules that involve expression of class to any object property

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Anguita, Alberto, Miguel García-Remesal, Diana de la Iglesia, and Victor Maojo. "NCBI2RDF: Enabling Full RDF-Based Access to NCBI Databases." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/983805.

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RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over dif

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Ravichandran, S. "Design and Development of Great-Recital Processing Created Methodology for Refining Semantic-Created Amalgamated Data Dispensation Technology." Asian Journal of Science and Applied Technology 10, no. 1 (2021): 13–19. http://dx.doi.org/10.51983/ajsat-2021.10.1.2811.

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Recovering RDF datasets from appropriated information sources has become a fundamental interaction to accomplish the vision of the semantic web. The semantic web vision advances an everyday growing of the semantic diagram that requires some preparing upgrades including SPARQL end-point and combined questions to secure the interminable extension. The current progressed elite figuring engineering has been growing quickly to conquer numerous issues including execution. Elite registering can possibly be utilized to improve the unified SPARQL inquiry and generally speaking activity including the pe

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Bassiliades, Nick. "A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation." International Journal on Semantic Web and Information Systems 16, no. 1 (2020): 87–115. http://dx.doi.org/10.4018/ijswis.2020010105.

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Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on

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Sima, Ana Claudia, Christophe Dessimoz, Kurt Stockinger, Monique Zahn-Zabal, and Tarcisio Mendes de Farias. "A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL." F1000Research 8 (October 29, 2019): 1822. http://dx.doi.org/10.12688/f1000research.21027.1.

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The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish ortho

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Abdallah, Hussein, Waleed Afandi, and Essam Mansour. "Demonstration of SPARQL ^ML : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3974–77. http://dx.doi.org/10.14778/3611540.3611599.

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This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL ML query. Our SPARQL ML query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the

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Ochieng, Peter. "PAROT: Translating natural language to SPARQL." Expert Systems with Applications 176 (August 2021): 114712. http://dx.doi.org/10.1016/j.eswa.2021.114712.

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Potoniec, Jedrzej. "Learning SPARQL Queries from Expected Results." Computing and Informatics 38, no. 3 (2019): 679–700. http://dx.doi.org/10.31577/cai_2019_3_679.

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Gomathi, R. "Efficient Optimization of Multiple SPARQL Queries." IOSR Journal of Computer Engineering 8, no. 6 (2013): 97–101. http://dx.doi.org/10.9790/0661-08697101.

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Chekol, Melisachew Wudage, Jérôme Euzenat, Pierre Genevès, and Nabil Layaïda. "SPARQL Query Containment Under SHI Axioms." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 10–16. http://dx.doi.org/10.1609/aaai.v26i1.8108.

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SPARQL query containment under schema axioms is the problem of determining whether, for any RDF graph satisfying a given set of schema axioms, the answers to a query are contained in the answers of another query. This problem has major applications for verification and optimization of queries. In order to solve it, we rely on the mu-calculus. Firstly, we provide a mapping from RDF graphs into transition systems. Secondly, SPARQL queries and RDFS and SHI axioms are encoded into mu-calculus formulas. This allows us to reduce query containment and equivalence to satisfiability in the mu-calculus.

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Stolpe, Audun. "A logical characterisation of SPARQL federation." Semantic Web 6, no. 6 (2014): 565–84. http://dx.doi.org/10.3233/sw-140160.

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Rietveld, Laurens, and Rinke Hoekstra. "The YASGUI family of SPARQL clients1." Semantic Web 8, no. 3 (2016): 373–83. http://dx.doi.org/10.3233/sw-150197.

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Ri Kim, Ju, Zhanfang Zhao, and Sung Kook Han. "Sparql query processing in relational databases." International Journal of Engineering & Technology 7, no. 3.3 (2018): 84. http://dx.doi.org/10.14419/ijet.v7i2.33.13860.

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Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach.Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SP

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McCarthy, Luke, Ben Vandervalk, and Mark Wilkinson. "SPARQL Assist language-neutral query composer." BMC Bioinformatics 13, Suppl 1 (2011): S2. http://dx.doi.org/10.1186/1471-2105-13-s1-s2.

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Barbieri, Davide Francesco, Daniele Braga, Stefano Ceri, Emanuele Della Valle, and Michael Grossniklaus. "Querying RDF streams with C-SPARQL." ACM SIGMOD Record 39, no. 1 (2010): 20–26. http://dx.doi.org/10.1145/1860702.1860705.

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Analyti, Anastasia. "Why provenance of SPARQL 1.1 queries." International Journal of Web Engineering and Technology 19, no. 3 (2024): 232–66. http://dx.doi.org/10.1504/ijwet.2024.142214.

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Sequeda, Juan F., and Daniel P. Miranker. "Ultrawrap: SPARQL execution on relational data." Journal of Web Semantics 22 (October 2013): 19–39. http://dx.doi.org/10.1016/j.websem.2013.08.002.

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Punnoose, Roshan, Adina Crainiceanu, and David Rapp. "SPARQL in the cloud using Rya." Information Systems 48 (March 2015): 181–95. http://dx.doi.org/10.1016/j.is.2013.07.001.

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Wimalaratne, S. M., J. Bolleman, N. Juty, et al. "SPARQL-enabled identifier conversion with Identifiers.org." Bioinformatics 31, no. 11 (2015): 1875–77. http://dx.doi.org/10.1093/bioinformatics/btv064.

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Ochieng, Peter. "PAROT: Translating natural language to SPARQL." Expert Systems with Applications: X 5 (April 2020): 100024. http://dx.doi.org/10.1016/j.eswax.2020.100024.

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Chebotko, Artem, Shiyong Lu, and Farshad Fotouhi. "Semantics preserving SPARQL-to-SQL translation." Data & Knowledge Engineering 68, no. 10 (2009): 973–1000. http://dx.doi.org/10.1016/j.datak.2009.04.001.

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Wiśniewski, Dawid, Jędrzej Potoniec, and Agnieszka Ławrynowicz. "BigCQ: Generating a Synthetic Set of Competency Questions Formalized into SPARQL-OWL (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13079–80. http://dx.doi.org/10.1609/aaai.v36i11.21676.

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We present a method for constructing synthetic datasets of Competency Questions translated into SPARQL-OWL queries. This method is used to generate BigCQ, the largest set of CQ patterns and SPARQL-OWL templates that can provide translation examples to automate assessing the completeness and correctness of ontologies.

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Ondo, AnicetLepetit, Laurence Capus, and Mamadou Bousso. "Enhancing SPARQL Query Rewriting for Complex Ontology Alignments." International journal of Web & Semantic Technology 16, no. 2 (2025): 01–17. https://doi.org/10.5121/ijwest.2025.16201.

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Abstract:

SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c : c), makes this process challenging. Existing approaches primarily focus on simple (s : s) and partially complex (s : c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non-expert users seeking to fully exploit the knowledge encapsulated in ontologies.This article proposes an innovative appr

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