research-article

Declarative RDF graph generation from heterogeneous (semi-)structured data: : A systematic literature review

Authors:

Dylan Van Assche,

Gerald Haesendonck,

Pieter Heyvaert,

Ben De Meester,

Anastasia DimouAuthors Info & Claims

Volume 75, Issue C

https://doi.org/10.1016/j.websem.2022.100753

Published: 01 January 2023 Publication History

Abstract

More and more data in various formats are integrated into knowledge graphs. However, there is no overview of existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data, making it difficult to select the right one for a certain use case. To support better decision making, we study the existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data relying on mapping languages. In this paper, we investigated existing mapping languages for schema and data transformations, and corresponding materialization and virtualization systems that generate knowledge graphs. We gather and unify 52 articles regarding knowledge graph generation from heterogeneous (semi-)structured data. We assess 15 characteristics on mapping languages for schema transformations, 5 characteristics for data transformations, and 14 characteristics for systems. Our survey paper provides an overview of the mapping languages and systems proposed the past two decades. Our work paves the way towards a better adoption of knowledge graph generation, as the right mapping language and system can be selected for each use case.

Highlights

•

A survey of RDF graph generation approaches from heterogeneous data.

•

An overview of mapping languages and their corresponding implementations.

•

A set of characteristics analyzed for each approach.

References

[1]

Hogan A., Blomqvist E., Cochez M., d’Amato C., de Melo G., Gutiérrez C., Gayo J.E.L., Kirrane S., Neumaier S., Polleres A., Navigli R., Ngomo A.N., Rashid S.M., Rula A., Schmelzeisen L., Sequeda J.F., Staab S., Zimmermann A., Knowledge graphs, 2020, CoRR abs/2003.02320. URL: https://arxiv.org/abs/2003.02320. arXiv:2003.02320.

[2]

Raimond Y., Ferne T., Smethurst M., Adams G., The BBC world service archive prototype, J. Web Semant. 27–28 (2014) 2–9,. URL: https://www.sciencedirect.com/science/article/pii/S1570826814000535. Semantic Web Challenge 2013.

Digital Library

[3]

Shadbolt N., O’Hara K., Linked data in government, IEEE Internet Comput. 17 (2013) 72–77,.

Digital Library

[4]

Holm J., Thomas G., Hendler J., Musialek C., US government linked open data: Semantic.data.gov, IEEE Intell. Syst. 27 (03) (2012) 25–31,.

Digital Library

[5]

Singhal A., Introducing the knowledge graph: things, not strings, 2012, URL: https://www.blog.google/products/search/introducing-knowledge-graph-things-not.

[6]

Shrivastava S., Bring rich knowledge of people, places, things and local businesses to your apps, 2017, URL: https://blogs.bing.com/search-quality-insights/2017-07/bring-rich-knowledge-of-people-places-things-and-local-businesses-to-your-apps.

[7]

Krishnan A., Making search easier: How amazon’s product graph is helping customers find products more easily, 2018, URL: https://blog.aboutamazon.com/innovation/making-search-easier.

[8]

Pittman R.J., Srivastava A., Hewavitharana S., Kale A., Mansour S., Cracking the code on conversational commerce, 2017, URL: https://www.ebayinc.com/stories/news/cracking-the-code-on-conversational-commerce.

[9]

Noy N., Gao Y., Jain A., Narayanan A., Patterson A., Taylor J., Industry-scale knowledge graphs: Lessons and challenges, Commun. ACM 62 (8) (2019) 36–43,.

Digital Library

[10]

He Q., Chen B.-C., Agarwal D., Building the linkedin knowledge graph, 2016, URL: https://engineering.linkedin.com/blog/2016/10/building-the-linkedin-knowledge-graph.

[11]

Chang S., Scaling knowledge access and retrieval at airbnb, 2018, URL: https://medium.com/airbnb-engineering/scaling-knowledge-access-and-retrieval-at-airbnb-665b6ba21e95.

[12]

Devarajan D., Happy birthday watson discovery, 2017, URL: https://www.ibm.com/blogs/bluemix/2017/12/happy-birthday-watson-discovery.

[13]

Hamad F., Liu I., Zhang X.X., Food discovery with uber eats: Building a query understanding engine, 2018, URL: https://eng.uber.com/uber-eats-query-understanding.

[14]

Hazber M., Li R., Li B., Zhao Y., Alalayah K., A survey: Transformation for integrating relational database with semantic web, in: Proceedings of ICMSS 2019, 2019, pp. 66–73,.

Digital Library

[15]

Hert M., Reif G., Gall H.C., A comparison of RDB-to-RDF mapping languages, in: Proc. of the 7^th International Conference on Semantic Systems, ACM, 2011, pp. 25–32,. URL: http://doi.acm.org/10.1145/2063518.2063522.

Digital Library

[16]

Fiorelli M., Stellato A., Lifting tabular data to RDF: A survey, in: Garoufallou E., Ovalle-Perandones M.-A. (Eds.), Metadata and Semantic Research, Springer International Publishing, Cham, 2021, pp. 85–96.

[17]

Spanos D.-E., Stavrou P., Mitrou N., Bringing relational databases into the semantic web: A survey, Semant. Web 3 (2012) 169–209,.

[18]

Bikakis N., Tsinaraki C., Gioldasis N., Stavrakantonakis I., Christodoulakis S., The XML and semantic web worlds: Technologies, interoperability and integration. a survey of the state of the art, 2016, arXiv e-prints arXiv:1608.03556.

[19]

Xiao G., Calvanese D., Kontchakov R., Lembo D., Poggi A., Rosati R., Zakharyaschev M., Ontology-Based Data Access: A Survey, IJCAI Organization, 2018.

[20]

Xiao G., Ding L., Cogrel B., Calvanese D., Virtual knowledge graphs: An overview of systems and use cases, Data Intell. 1 (3) (2019) 201–223,. arXiv:https://direct.mit.edu/dint/article-pdf/1/3/201/683759/dint_a_00011.pdf.

[21]

Ryen V., Soylu A., Roman D., Building semantic knowledge graphs from (semi-)structured data: A review, Future Internet 14 (5) (2022) URL: https://www.mdpi.com/1999-5903/14/5/129.

[22]

Tamašauskaitė G., Groth P., Defining a knowledge graph development process through a systematic review, ACM Trans. Softw. Eng. Methodol. (2022),.

Digital Library

[23]

Chaves-Fraga D., Priyatna F., Cimmino A., Toledo J., Ruckhaus E., Corcho O., GTFS-madrid-bench: A benchmark for virtual knowledge graph access in the transport domain, J. Web Semant. 65 (2020),. URL: https://www.sciencedirect.com/science/article/pii/S1570826820300354.

[24]

J. Arenas-Guerrero, M. Scrocca, A. Iglesias-Molina, J. Toledo, L.P. Gilo, D. Dona, O. Corcho, D. Chaves-Fraga, Knowledge graph construction with R2RML and RML: An ETL system-based overview, in: Proceedings of the 2nd International Workshop on Knowledge Graph Construction, 2021.

[25]

Chaves-Fraga D., Endris K.M., Iglesias E., Corcho O., Vidal M.-E., What are the parameters that affect the construction of a knowledge graph?, in: OTM Confederated International Conferences “on the Move to Meaningful Internet Systems”, Springer, 2019, pp. 695–713.

[26]

Fagin R., Kolaitis P.G., Popa L., Tan W.-C., Composing schema mappings: Second-order dependencies to the rescue, ACM Trans. Database Syst. 30 (4) (2005) 994–1055,.

Digital Library

[27]

Lenzerini M., Data integration: a theoretical perspective, in: PODS ’02, 2002.

[28]

Arenas M., Pérez J., Riveros C., The recovery of a schema mapping: Bringing exchanged data back, 2009.

[29]

Hyland B., Atemezing G.A., Villazón-Terrazas B., Best Practices for Publishing Linked Data, World Wide Web Consortium (W3C), 2014, URL: https://www.w3.org/TR/ld-bp/.

[30]

Rahm E., Do H.H., Data cleaning: Problems and current approaches, IEEE Data Eng. Bull. 23 (4) (2000) 3–13.

Digital Library

[31]

Vassalos V., Liu L., Özsu M.T. (Eds.), Answering Queries Using Views, Springer US, Boston, MA, 2009, pp. 92–98,.

[32]

Zaveri A., Rula A., Maurino A., Pietrobon R., Lehmann J., Auer S., Quality assessment for linked data: A survey, Semant. Web 7 (2015) 63–93,.

[33]

Issa S., Adekunle O., Hamdi F., Cherfi S.S.-S., Dumontier M., Zaveri A., Knowledge graph completeness: A systematic literature review, IEEE Access 9 (2021) 31322–31339,.

[34]

Verreydt S., Yskout K., Joosen W., Security and privacy requirements for electronic consent: A systematic literature review, ACM Trans. Comput. Healthc. 2 (2) (2021),.

Digital Library

[35]

Rahmani A.M., Azhir E., Ali S., Mohammadi M., Ahmed O.H., Yassin Ghafour M., Hasan Ahmed S., Hosseinzadeh M., Artificial intelligence approaches and mechanisms for big data analytics: a systematic study, Comput. Sci. (2021).

[36]

Kitchenham B., Procedures for performing systematic reviews, Keele, UK, Keele Univ. 33 (2004) (2004) 1–26.

[37]

Kitchenham B., Procedures for performing systematic reviews, Keele, UK, Keele Univ. 33 (2004).

[38]

Moher D., Liberati A., Tetzlaff J., Altman D., Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, Br. Med. J. 8 (2009) 336–341,.

[39]

Klyne G., Carroll J.J., Resource Description Framework (RDF): Concepts and Abstract Syntax, World Wide Web Consortium (W3C), 2004, URL: http://www.w3.org/TR/rdf-concepts/.

[40]

Dell’Aglio D., Polleres A., Lopes N., Bischof S., Querying the web of data with XSPARQL 1.1, in: SEMWEB, 2014.

[41]

Chaves-Fraga D., Priyatna F., Alobaid A., Corcho O., Exploiting declarative mapping rules for generating GraphQL servers with morph-GraphQL, Int. J. Softw. Eng. Knowl. Eng. 30 (06) (2020) 785–803,.

[42]

De Meester B., Seymoens T., Dimou A., Verborgh R., Implementation-independent function reuse, Future Gener. Comput. Syst. 110 (2020) 946–959,. URL: https://ben.de-meester.org/research/preprints/DeMeester2019Implementation.pdf.

[43]

Corby O., Faron-Zucker C., Gandon F., LDScript: A linked data script language, in: d’Amato C., Fernandez M., Tamma V., Lecue F., Cudré-Mauroux P., Sequeda J., Lange C., Heflin J. (Eds.), The Semantic Web – ISWC 2017, Springer International Publishing, Cham, 2017, pp. 208–224.

[44]

Le-Phuoc D., Nguyen-Mau H.Q., Parreira J.X., Hauswirth M., A middleware framework for scalable management of linked streams, J. Web Semant. 16 (2012) 42–51,. URL: https://www.sciencedirect.com/science/article/pii/S1570826812000728. The Semantic Web Challenge 2011.

Digital Library

[45]

Scrocca M., Comerio M., Carenini A., Celino I., Turning transport data to comply with EU standards while enabling a multimodal transport knowledge graph, in: International Semantic Web Conference, Springer, 2020, pp. 411–429.

[46]

Lefrançois M., Zimmermann A., Bakerally N., A SPARQL extension for generating RDF from heterogeneous formats, in: The Semantic Web 14^th International Conference, ESWC 2017, PortoroŽ, Slovenia, May 28 – June 1, 2017, Proceedings, Springer International Publishing, Portoroz, Slovenia, 2017, pp. 35–50. URL: http://www.maxime-lefrancois.info/docs/LefrancoisZimmermannBakerally-ESWC2017-Generate.pdf.

[47]

De Meester B., Dimou A., Verborgh R., Mannens E., Detailed provenance capture of data processing, in: Garijo D., van Hage W.R., Kauppinen T., Kuhn T., Zhao J. (Eds.), Proceedings of the First Workshop on Enabling Open Semantic Science (SemSci), in: CEUR Workshop Proceedings, vol. 1931, CEUR, 2017, pp. 31–38. URL: http://ceur-ws.org/Vol-1931/#paper-05.

[48]

Chortaras A., Stamou G., Mapping diverse data to RDF in practice, in: Vrandečić D., Bontcheva K., Suárez-Figueroa M.C., Presutti V., Celino I., Sabou M., Kaffee L.-A., Simperl E. (Eds.), The Semantic Web – ISWC 2018, in: Lecture Notes in Computer Science, vol. 11136, Springer, Cham, 2018, pp. 441–457,.

Digital Library

[49]

Vu B., Pujara J., Knoblock C.A., D-REPR: A language for describing and mapping diversely-structured data sources to RDF, in: Proceedings of the 10th International Conference on Knowledge Capture, in: K-CAP ’19, Association for Computing Machinery, New York, NY, USA, 2019, pp. 189–196,.

Digital Library

[50]

Lefrançois M., Zimmermann A., Bakerally N., Flexible RDF generation from RDF and heterogeneous data sources with SPARQL-generate, in: Ciancarini P., Poggi F., Horridge M., Zhao J., Groza T., Suarez-Figueroa M.C., d’Aquin M., Presutti V. (Eds.), Knowledge Engineering and Knowledge Management, Springer International Publishing, Cham, 2017, pp. 131–135.

[51]

Kyzirakos K., Savva D., Vlachopoulos I., Vasileiou A., Karalis N., Koubarakis M., Manegold S., GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings, J. Web Semant. 52–53 (2018) 16–32,. URL: https://www.sciencedirect.com/science/article/pii/S1570826818300428.

[52]

K. Kyzirakos, GeoTriples: a tool for publishing geospatial data as RDF graphs using R2RML mappings, 12.

[53]

Van Assche D., Haesendonck G., De Mulder G., Delva T., Heyvaert P., De Meester B., Dimou A., Leveraging web of things W3C recommendations for knowledge graphs generation, in: Brambilla M., Chbeir R., Frasincar F., Manolescu I. (Eds.), Web Engineering, Springer International Publishing, Cham, 2021, pp. 337–352.

[54]

Bischof S., Decker S., Krennwallner T., Lopes N., Polleres A., Mapping between RDF and XML with XSPARQL, J. Data Semant. 1 (3) (2012) 147–185,.

[55]

Dimou A., Vander Sande M., Colpaert P., Verborgh R., Mannens E., Van de Walle R., RML: A generic language for integrated RDF mappings of heterogeneous data, in: Bizer C., Heath T., Auer S., Berners-Lee T. (Eds.), Proceedings of the 7^th Workshop on Linked Data on the Web, in: CEUR Workshop Proceedings, vol. 1184, CEUR-WS.org, 2014, URL: http://ceur-ws.org/Vol-1184/ldow2014_paper_01.pdf.

[56]

Lopes N., Bischof S., Decker S., Polleres A., On the semantics of heterogeneous querying of relational, XML and RDF data with XSPARQL, in: Proceedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA 2011), Lisbon, Portugal, Citeseer, 2011, pp. 10–13.

[57]

García-González H., Boneva I., Staworko S., Labra-Gayo J.E., Lovelle J.M.C., ShExML: improving the usability of heterogeneous data mapping languages for first-time users, PeerJ Comput. Sci. 6 (2020) e318.

[58]

Michel F., Djimenou L., Faron-Zucker C., Montagnat J., Translation of relational and non-relational databases into RDF with xR2RML, 2015,.

[59]

Xiao G., Lanti D., Kontchakov R., Komla-Ebri S., Güzel-Kalaycı E., Ding L., Corman J., Cogrel B., Calvanese D., Botoeva E., The virtual knowledge graph system ontop, in: Pan J.Z., Tamma V., d’Amato C., Janowicz K., Fu B., Polleres A., Seneviratne O., Kagal L. (Eds.), The Semantic Web – ISWC 2020, Springer International Publishing, Cham, 2020, pp. 259–277.

[60]

Pankowski T., Bąk J., DAFO: An ontological database system with faceted queries, in: Hitzler P., Kirrane S., Hartig O., de Boer V., Vidal M.-E., Maleshkova M., Schlobach S., Hammar K., Lasierra N., Stadtmüller S., Hose K., Verborgh R. (Eds.), The Semantic Web: ESWC 2019 Satellite Events, Springer International Publishing, Cham, 2019, pp. 152–155.

[61]

Calbimonte J.-P., Corcho O., Gray A.J.G., Enabling ontology-based access to streaming data sources, in: Patel-Schneider P.F., Pan Y., Hitzler P., Mika P., Zhang L., Pan J.Z., Horrocks I., Glimm B. (Eds.), The Semantic Web – ISWC 2010, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 96–111.

[62]

Michel F., Faron-Zucker C., Montagnat J., A generic mapping-based query translation from SPARQL to various target database query languages, in: Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, SciTePress, INSTICC, 2016, pp. 147–158,.

[63]

De Meester B., Maroy W., Dimou A., Verborgh R., Mannens E., Blomqvist E., Maynard D., Gangemi A., Hoekstra R., Hitzler P., Hartig O. (Eds.), Declarative Data Transformations for Linked Data Generation: the case of DBpedia, in: Lecture Notes in Computer Science, vol. 10250, Springer, Cham, 2017, pp. 33–48,. URL: https://link.springer.com/chapter/10.1007/978-3-319-58451-5_3.

Digital Library

[64]

Priyatna F., Corcho O., Sequeda J., Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph, in: Proceedings of the 23^rd International Conference on World Wide Web, WWW ’14, Association for Computing Machinery, Seoul, Korea, 2014, pp. 479–490.

[65]

Jozashoori S., Chaves-Fraga D., Iglesias E., Vidal M.-E., Corcho O., FunMap: Efficient execution of functional mappings for knowledge graph creation, in: International Semantic Web Conference, Springer, 2020, pp. 276–293.

[66]

Junior A.C., Debruyne C., Brennan R., O’Sullivan D., FunUL: A method to incorporate functions into uplift mapping languages, in: Proceedings of the 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS ’16, Association for Computing Machinery, New York, NY, USA, 2016, pp. 267–275,.

Digital Library

[67]

Debruyne C., O’Sullivan D., R2RML-F: Towards sharing and executing domain logic in R2RML mappings, in: Workshop on Linked Data on the Web, in: CEUR Workshop Proceedings, CEUR, 2016.

[68]

Maroy W., Dimou A., Kontokostas D., De Meester B., Verborgh R., Lehmann J., Mannens E., Hellmann S., d’Amato C., Fernandez M., Tamma V., Lecue F., Cudré-Mauroux P., Sequeda J., Lange C., Heflin J. (Eds.), Sustainable Linked Data Generation: The Case of DBpedia, in: Lecture Notes in Computer Science, vol. 10588, Springer, Cham, Vienna, Austria, 2017, pp. 297–313,. URL: http://jens-lehmann.org/files/2017/iswc_dbpedia_rml.pdf.

Digital Library

[69]

Atzori M., Mika P., Tudorache T., Bernstein A., Welty C., Knoblock C., Vrandečić D., Groth P., Noy N., Janowicz K., Goble C. (Eds.), Toward the Web of Functions: Interoperable Higher-Order Functions in SPARQL, in: Lecture Notes in Computer Science, vol. 8797, Springer, Cham, 2014, pp. 406–421,.

Digital Library

[70]

Slepicka J., Yin C., Szekely P.A., Knoblock C.A., KR2RML: An alternative interpretation of R2RML for heterogenous sources, in: Proceedings of the 6^th International Workshop on Consuming Linked Data (COLD 2015), 2015, URL: http://usc-isi-i2.github.io/papers/slepicka15-cold.pdf.

[71]

A. Schultz, A. Matteini, R. Isele, C. Bizer, C. Becker, Linked Data Integration Framework, 6.

[72]

Dimou A., Sande M.V., Slepicka J., Szekely P., Mannens E., Knoblock C., Walle R.V.d., Mapping hierarchical sources into RDF using the RML mapping language, in: 2014 IEEE International Conference on Semantic Computing, 2014, pp. 151–158,.

Digital Library

[73]

Jozashoori S., Vidal M.-E., MapSDI: A scaled-up semantic data integration framework for knowledge graph creation, in: Panetto H., Debruyne C., Hepp M., Lewis D., Ardagna C.A., Meersman R. (Eds.), On the Move to Meaningful Internet Systems: OTM 2019 Conferences, Springer International Publishing, Cham, 2019, pp. 58–75.

[74]

Haesendonck G., Maroy W., Heyvaert P., Verborgh R., Dimou A., Parallel RDF generation from heterogeneous big data, in: Groppe S., Gruenwald L. (Eds.), Proceedings of the International Workshop on Semantic Big Data - SBD ’19, ACM Press, Amsterdam, Netherlands, 2019,. URL: https://biblio.ugent.be/publication/8619808/file/8659668.pdf.

Digital Library

[75]

G.M. Santipantakis, K.I. Kotis, G.A. Vouros, C. Doulkeridis, RDF-Gen: Generating RDF from streaming and archival data, in: Proceedings of the 8^th International Conference on Web Intelligence, Mining and Semantics, 2018, pp. 1–10.

[76]

Simsek U., Kärle E., Fensel D., RocketRML - a NodeJS implementation of a use case specific RML mapper, 2019, arXiv abs/1903.04969.

[77]

Iglesias E., Jozashoori S., Chaves-Fraga D., Collarana D., Vidal M.-E., SDM-RDFizer: An RML interpreter for the efficient creation of rdf knowledge graphs, in: Proceedings of the 29^th ACM International Conference on Information & Knowledge Management, ACM, 2020,.

Digital Library

[78]

Mauri A., Calbimonte J.-P., Dell’Aglio D., Balduini M., Brambilla M., Della Valle E., Aberer K., TripleWave: Spreading RDF streams on the web, in: Groth P., Simperl E., Gray A., Sabou M., Krötzsch M., Lecue F., Flöck F., Gil Y. (Eds.), The Semantic Web – ISWC 2016, Springer International Publishing, Cham, 2016, pp. 140–149,.

Digital Library

[79]

Llaves A., Corcho Ó., Taylor P., Taylor K.L., Enabling RDF stream processing for sensor data management in the environmental domain, Int. J. Semant. Web Inf. Syst. 12 (2016) 1–21.

[80]

Unbehauen J., Martin M., Executing SPARQL queries over mapped document store with SparqlMap-M, in: Proceedings of the 12th International Conference on Semantic Systems, in: SEMANTiCS 2016, Association for Computing Machinery, New York, NY, USA, 2016, pp. 137–144,.

Digital Library

[81]

Buron M., Goasdoué F., Manolescu I., Mugnier M.-L., Obi-Wan: Ontology-based RDF integration of heterogeneous data, Proc. VLDB Endow. 13 (12) (2020) 2933–2936,.

Digital Library

[82]

Endris K.M., Rohde P.D., Vidal M.-E., Auer S., Ontario: Federated query processing against a semantic data lake, in: Hartmann S., Küng J., Chakravarthy S., Anderst-Kotsis G., Tjoa A.M., Khalil I. (Eds.), Database and Expert Systems Applications: 30^th International Conference, DEXA, Part I, in: Lecture Notes in Computer Science, vol. 11706, Springer, Cham, 2019, pp. 379–395,.

Digital Library

[83]

Kalayci E.G., Brandt S., Calvanese D., Ryzhikov V., Xiao G., Zakharyaschev M., Ontology–based access to temporal data with ontop: A framework proposal, Int. J. Appl. Math. Comput. Sci. 29 (1) (2019) 17–30,.

Digital Library

[84]

Rodríguez-Muro M., Kontchakov R., Zakharyaschev M., Ontology-based data access: Ontop of databases, in: Alani H., Kagal L., Fokoue A., Groth P., Biemann C., Parreira J.X., Aroyo L., Noy N., Welty C., Janowicz K. (Eds.), The Semantic Web – ISWC 2013, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 558–573.

[85]

Calvanese D., Cogrel B., Komla-Ebri S., Kontchakov R., Lanti D., Rezk M., Rodriguez-Muro M., Xiao G., Ontop: Answering SPARQL queries over relational databases, Semant. Web J. 8 (3) (2017) 471–487,.

Digital Library

[86]

Bereta K., Xiao G., Koubarakis M., Ontop-spatial: Ontop of geospatial databases, J. Web Semant. 58 (2019),. URL: https://www.sciencedirect.com/science/article/pii/S1570826819300447.

Digital Library

[87]

Kharlamov E., Jiménez-Ruiz E., Zheleznyakov D., Bilidas D., Giese M., Haase P., Horrocks I., Kllapi H., Koubarakis M., Özçep Ö., Rodríguez-Muro M., Rosati R., Schmidt M., Schlatte R., Soylu A., Waaler A., Optique: Towards OBDA systems for industry, in: Cimiano P., Fernández M., Lopez V., Schlobach S., Völker J. (Eds.), The Semantic Web: ESWC 2013 Satellite Events, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 125–140.

[88]

Giese M., Soylu A., Vega-Gorgojo G., Waaler A., Haase P., Jimenez-Ruiz E., Lanti D., Rezk M., Xiao G., Ozcep O., Rosati R., Optique: Zooming in on big data, Computer 48 (03) (2015) 60–67,.

Digital Library

[89]

Mami M.N., Graux D., Scerri S., Jabeen H., Auer S., Lehmann J., Ghidini C., Hartig O., Maleshkova M., Svátek V., Cruz I., Hogan A., Song J., Lefrançois M., Gandon F. (Eds.), Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources, in: Lecture Notes in Computer Science, vol. 11779, Springer, Cham, 2019, pp. 229–245,.

Digital Library

[90]

Calbimonte J.-P., Sarni S., Eberle J., Aberer K., XGSN: An open-source semantic sensing middleware for the web of things, in: TC/SSN@ISWC, 2014.

[91]

Das S., Sundara S., Cyganiak R., R2RML: RDB to RDF Mapping Language, World Wide Web Consortium (W3C), 2012, URL: http://www.w3.org/TR/r2rml/.

[92]

Prud’hommeaux E., Seaborne A., SPARQL Query Language for RDF, World Wide Web Consortium (W3C), 2008, URL: http://www.w3.org/TR/rdf-sparql-query/.

[93]

Prud’hommeaux E., Boneva I., Labra Gayo J.E., Kellogg G., Shape Expressions Language 2.1, World Wide Web Consortium (W3C), 2018, URL: http://shex.io/shex-semantics/.

[94]

Daga E., Asprino L., Mulholland P., Gangemi A., Facade-x: an opinionated approach to SPARQL anything, 2021, arXiv preprint arXiv:2106.02361.

[95]

De Meester B., Heyvaert P., Verborgh R., Dimou A., Mapping languages: analysis of comparative characteristics, in: Proceedings of First Knowledge Graph Building Workshop, 2019, URL: https://openreview.net/forum?id=HklWL4erv4.

[96]

Van Assche D., Haesendonck G., De Mulder G., Delva T., Heyvaert P., De Meester B., Dimou A., Leveraging web of things W3C recommendations for knowledge graphs generation, in: Brambilla M., Chbeir R., Frasincar F., Manolescu I. (Eds.), Web Engineering, in: Lecture Notes in Computer Science, vol. 12706, Springer, Cham, 2021, pp. 337–352,.

Digital Library

[97]

Dimou A., Verborgh R., Sande M.V., Mannens E., de Walle R.V., Machine-interpretable dataset and service descriptions for heterogeneous data access and retrieval, in: Proceedings of the 11^th International Conference on Semantic Systems - SEMANTICS ’15, ACM Press, 2015,.

Digital Library

[98]

Delva T., Van Assche D., Heyvaert P., De Meester B., Dimou A., Integrating nested data into knowledge graphs with RML fields, in: Chaves-Fraga D., Dimou A., Heyvaert P., Priyatna F., Sequeda J. (Eds.), Proceedings of the 2^nd International Workshop on Knowledge Graph Construction Co-Located with 18^th Extended Semantic Web Conference (ESWC 2021), Vol. 2873, CEUR, 2021, URL: http://ceur-ws.org/Vol-2873/paper9.pdf.

[99]

Chortaras A., Stamou G., D2RML: Integrating heterogeneous data and web services into custom rdf graphs, in: LDOW@WWW, 2018.

[100]

O. Ben-Kiki, C. Evans, I. döt Net, YAML Ain’t Markup Language (YAML™) Version 1.2, Techreport, 2009, URL:.

[101]

Malhotra A., Melton J., Walsh N., Kay M., XQuery 1.0 and XPath 2.0 Functions and Operators (Second Edition), World Wide Web Consortium (W3C), 2015, URL: https://www.w3.org/TR/2010/REC-xpath-functions-20101214/.

[102]

Sporny M., Kellogg G., Lanthaler M., JSON-LD 1.0 – A JSON-based Serialization for Linked Data, World Wide Web Consortium (W3C), 2014, URL: http://www.w3.org/TR/json-ld/.

[103]

Corby O., Faron-Zucker C., STTL: A SPARQL-based transformation language for RDF, in: Proceedings of the 11th International Conference on Web Information Systems and Technologies - WEBIST, 2015,.

[104]

Arenas M., Bertails A., Prud’hommeaux E., Sequeda J., A Direct Mapping of Relational Data to RDF, World Wide Web Consortium (W3C), 2012, URL: http://www.w3.org/TR/rdb-direct-mapping/.

[105]

Barrasa J., Corcho O., Gomez-Perez A., R2O, an extensible and semantically based database-to-ontology mapping language, 2004.

[106]

Cyganiak R., Bizer C., Garbers J., Maresch O., Becker C., The D2RQ Mapping Language, FU Berlin, DERI, UCB, JP Morgan Chase, AGFA Healthcare, HP Labs, Johannes Kepler Universität Linz, 2012, URL: http://d2rq.org/d2rq-language.

[107]

N. Minadakis, Y. Marketakis, H. Kondylakis, G. Flouris, M. Theodoridou, M. Doerr, G. Jong, X3ML Framework: An effective suite for supporting data mappings, 2015.

[108]

Marketakis Y., Minadakis N., Kondylakis H., Konsolaki K., Samaritakis G., Theodoridou M., Flouris G., Doerr M., X3ml mapping framework for information integration in cultural heritage and beyond, Internat. J. Digit. Libraries 18 (4) (2017) 301–319.

[109]

Heyvaert P., De Meester B., Dimou A., Verborgh R., Declarative rules for linked data generation at your fingertips!, in: Gangemi A., Gentile A.L., Nuzzolese A.G., Rudolph S., Maleshkova M., Paulheim H., Pan J.Z., Alam M. (Eds.), The Semantic Web: ESWC 2018 Satellite Events, in: Lecture Notes in Computer Science, vol. 11155, Springer, Cham, Heraklion, Crete, Greece, 2018,. URL: https://2018.eswc-conferences.org/files/posters-demos/paper_297.pdf.

Digital Library

[110]

Battle R., Kolas D., GeoSPARQL: enabling a geospatial semantic web, Semantic Web J. 3 (4) (2011) 355–370.

[111]

Kyzirakos K., Karpathiotakis M., Koubarakis M., Strabon: A semantic geospatial DBMS, in: International Semantic Web Conference, Springer, 2012, pp. 295–311.

[112]

Bereta K., Smeros P., Koubarakis M., Representation and querying of valid time of triples in linked geospatial data, in: Extended Semantic Web Conference, Springer, 2013, pp. 259–274.

[113]

M. Perry, J. Herring, OGC GeoSPARQL - A Geographic Query Language for RDF Data, Technical Report, 2012, URL:.

[114]

Bizer C., D2R MAP – a database to RDF mapping language, 2003.

[115]

Le Phuoc D., Dao-Tran M., Le Tuan A., Duc M.N., Hauswirth M., RDF stream processing with CQELS framework for real-time analysis, in: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS ’15, Association for Computing Machinery, New York, NY, USA, 2015, pp. 285–292,.

Digital Library

[116]

Kaebisch S., Kamiya T., McCool M., Charpenay V., Kovatsch M., Web of Things (WoT) Thing Description, World Wide Web Consortium (W3C), 2020, URL: http://www.w3.org/TR/wot-thing-description/.

[117]

Makinouchi A., A consideration on normal form of not-necessarily-normalized relation in the relational data model, in: VLDB, Vol. 1977, Citeseer, 1977, pp. 447–453.

[118]

Barbieri D.F., Braga D., Ceri S., Della Valle E., Grossniklaus M., C-SPARQL: SPARQL for continuous querying, in: Proceedings of the 18^th International Conference on World Wide Web, ACM, New York, USA, 2009, pp. 1061–1062,.

Digital Library

[119]

Brenninkmeijer C.Y.A., Galpin I., Fernandes A.A.A., Paton N.W., A semantics for a query language over sensors, streams and relations, in: Gray A., Jeffery K., Shao J. (Eds.), Sharing Data, Information and Knowledge, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 87–99.

[120]

Calvanese D., De Giacomo G., Lembo D., Lenzerini M., Rosati R., Tractable reasoning and efficient query answering in description logics: The DL-lite family, J. Automat. Reason. 39 (3) (2007) 385–429.

[121]

Haller A., Janowicz K., Cox S., Le Phuoc D., Taylor K., Lefrançois M., Semantic Sensor Network Ontology, World Wide Web Consortium (W3C), 2017, URL: https://www.w3.org/TR/vocab-ssn/.

[122]

Unbehauen J., Stadler C., Auer S., Accessing relational data on the web with SparqlMap, in: Takeda H., Qu Y., Mizoguchi R., Kitamura Y. (Eds.), Semantic Technology, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 65–80.

[123]

Calbimonte J.-P., Jeung H., Corcho O., Aberer K., Enabling query technologies for the semantic sensor web, Int. J. Semant. Web Inf. Syst. (IJSWIS) 8 (1) (2012) 43–63.

[124]

K. Aberer, M. Hauswirth, A. Salehi, Global Sensor Networks, Technical Report, 2006.

[125]

Goncalves M., Chaves-Fraga D., Corcho O., Handling qualitative preferences in sparql over virtual ontology-based data access, 2022, pp. 659–682,.

[126]

Chaves-Fraga D., Ruckhaus E., Priyatna F., Vidal M.-E., Corcho O., Enhancing virtual ontology based access over tabular data with morph-CSV, Semant. Web (2021) 1–34,.

Digital Library

[127]

Arenas-Guerrero J., Chaves-Fraga D., Toledo J., S. Pérez M., Corcho O., Morph-KGC: Scalable knowledge graph materialization with mapping partitions, Semant. Web J. (2022).

[128]

Jozashoori S., Sakor A., Iglesias E., Vidal M.-E., Eablock: A declarative entity alignment block for knowledge graph creation pipelines, in: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 1908–1916,.

Digital Library

[129]

de Medeiros L.F., Priyatna F., Corcho O., MIRROR: Automatic R2RML mapping generation from relational databases, in: Cimiano P., Frasincar F., Houben G.-J., Schwabe D. (Eds.), Engineering the Web in the Big Data Era, Springer International Publishing, Cham, 2015, pp. 326–343.

[130]

Sicilia A., Nemirovski G., AutoMap4OBDA: Automated generation of R2RML mappings for OBDA, in: Knowledge Engineering and Knowledge Management, Springer International Publishing, Cham, 2016, pp. 577–592.

[131]

Jiménez-Ruiz E., Kharlamov E., Zheleznyakov D., Horrocks I., Pinkel C., Skjæveland M.G., Thorstensen E., Mora J., BootOX: Practical mapping of RDBs to OWL 2, in: Arenas M., Corcho O., Simperl E., Strohmaier M., d’Aquin M., Srinivas K., Groth P., Dumontier M., Heflin J., Thirunarayan K., Staab S. (Eds.), The Semantic Web - ISWC 2015, Springer International Publishing, Cham, 2015, pp. 113–132.

[132]

Jiménez-Ruiz E., Hassanzadeh O., Srinivas K., Efthymiou V., Chen J., SemTab 2019: Semantic web challenge on tabular data to knowledge graph matching, 2019, URL: http://ceur-ws.org/Vol-2553/.

[133]

Jiménez-Ruiz E., Hassanzadeh O., Efthymiou V., Chen J., Srinivas K., Cutrona V., SemTab 2020: Semantic web challenge on tabular data to knowledge graph matching 2020, 2020, URL: http://ceur-ws.org/Vol-2775/.

[134]

Jiménez-Ruiz E., Hassanzadeh O., Srinivas K., Efthymiou V., Chen J., SemTab 2021: Semantic web challenge on tabular data to knowledge graph matching, 2021, URL: http://ceur-ws.org/Vol-2553/.

[135]

Dimou A., Chaves-Fraga D., Declarative description of knowledge graphs construction automation: Status & challenges, 2022.

[136]

Mountantonakis M., Tzitzikas Y., Large-scale semantic integration of linked data: A survey, ACM Comput. Surv. 52 (5) (2019),.

Digital Library

Cited By

Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/1941221
Van Assche DChaves-Fraga DDimou A(2024)KROWN: A Benchmark for RDF Graph MaterialisationThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_2(20-39)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77847-6_2
Duan XChaves-Fraga DDimou A(2023)XSD2SHACL: Capturing RDF Constraints from XML SchemaProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627565(214-222)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627565
Show More Cited By

Index Terms

Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

Index terms have been assigned to the content through auto-classification.

Recommendations

A Method for Integrating Heterogeneous Data into a Knowledge Graph
Information Integration and Web Intelligence
Abstract
Knowledge graphs have gained popularity in many areas as a means of representing knowledge in a structured format. A large number of approaches for knowledge graph construction have been developed based on unstructured, semi-structured, or ...
Pure and declarative syntax definition: paradise lost and regained
OOPSLA '10

Syntax definitions are pervasive in modern software systems, and serve as the basis for language processing tools like parsers and compilers. Mainstream parser generators pose restrictions on syntax definitions that follow from their implementation ...
Declarative Data Transformations for Linked Data Generation: The Case of DBpedia
The Semantic Web
Abstract
Mapping languages allow us to define how Linked Data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked Data. Since complex data transformations remain out of scope for mapping languages, these ...

Comments

Information & Contributors

Information

Published In

cover image Web Semantics: Science, Services and Agents on the World Wide Web

Web Semantics: Science, Services and Agents on the World Wide Web Volume 75, Issue C

Jan 2023

166 pages

ISSN:1570-8268

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/1941221
Van Assche DChaves-Fraga DDimou A(2024)KROWN: A Benchmark for RDF Graph MaterialisationThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_2(20-39)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77847-6_2
Duan XChaves-Fraga DDimou A(2023)XSD2SHACL: Capturing RDF Constraints from XML SchemaProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627565(214-222)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627565
Iglesias-Molina AToledo JCorcho OChaves-Fraga D(2023)Re-Construction Impact on Metadata Representation ModelsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627554(197-205)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627554
Iglesias-Molina AVan Assche DArenas-Guerrero JDe Meester BDebruyne CJozashoori SMaria PMichel FChaves-Fraga DDimou A(2023)The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDFThe Semantic Web – ISWC 202310.1007/978-3-031-47243-5_9(152-175)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47243-5_9
Oo S(2023)A Window into the Multiple Views of Linked DataThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_51(331-340)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-43458-7_51

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents