Skip to main content
SpringerLink
Log in

Investigating Ontology-Based Data Access with GitHub

  • Conference paper
  • First Online:
The Semantic Web (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

Included in the following conference series:

  • 882 Accesses

Abstract

Data analysis-based decision-making is performed daily by domain experts. As data grows, getting access to relevant data becomes a challenge. In an approach known as Ontology-based data access (OBDA), ontologies are advocated as a suitable formal tool to address complex data access. This technique combines a domain ontology with a data source by using a declarative mapping specification to enable data access using a domain vocabulary. We investigate this approach by studying the theoretical background; conducting a literature review on the implementation of OBDA in production systems; implementing OBDA on a relational dataset using an OBDA tool and; providing results and analysis of query answering. We selected Ontop (https://ontop-vkg.org) to illustrate how this technique enhances the data usage of the GitHub community. Ontop is an open-source OBDA tool applied in the domain of relational databases. The implementation consists of the GHTorrent dataset and an extended SemanGit ontology. We perform a set of queries to highlight a subset of the features of this data access approach. The results look positive and can assist various use cases related to GitHub data with a semantic approach. OBDA does provide benefits in practice, such as querying in domain vocabulary and making use of reasoning over the axioms in the ontology. However, the practical impediments we observe are in the “manual” development of a domain ontology and the creation of a mapping specification which requires deep knowledge of a domain and the data. Also, implementing OBDA within the practical context of an information system requires careful consideration for a suitable user interface to facilitate the query construction from ontology vocabulary. Finally, we conclude with a summary of the paper and direction for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://d2rq.org/.

  2. 2.

    https://www.oracle.com/database/technologies/spatialandgraph.html.

  3. 3.

    https://www.stardog.com/.

  4. 4.

    https://github.com/about.

  5. 5.

    https://github.com/SemanGit/SemanGit.

  6. 6.

    https://protege.stanford.edu/.

  7. 7.

    https://www.w3.org/TeamSubmission/turtle/.

  8. 8.

    https://github.com/vuejs/vue.

  9. 9.

    https://ontopic.ai/en/.

References

  1. Abadi, D., et al.: The Seattle report on database research. ACM SIGMOD Rec. 48(4), 44–53 (2020). https://doi.org/10.1145/3385658.3385668

    Article  Google Scholar 

  2. Baader, F., Horrocks, I., Lutz, C., Sattler, U.: Introduction to Description Logic. Cambridge University Press, Cambridge (2017). https://doi.org/10.1017/9781139025355

    Book  MATH  Google Scholar 

  3. Ben Mahria, B., Chaker, I., Zahi, A.: A novel approach for learning ontology from relational database: from the construction to the evaluation. J. Big Data 8(1), 1–22 (2021). https://doi.org/10.1186/s40537-021-00412-2

    Article  Google Scholar 

  4. Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017). https://doi.org/10.3233/SW-160217

    Article  Google Scholar 

  5. Calvanese, D., et al.: The MASTRO system for ontology-based data access. Semant. Web 2(1), 43–53 (2011). https://doi.org/10.3233/SW-2011-0029

    Article  Google Scholar 

  6. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rosati, R.: Ontology-based database access. In: SEBD, pp. 324–331 (2007)

    Google Scholar 

  7. Calvanese, D., et al.: ADaMaP: automatic alignment of relational data sources using mapping patterns. In: La Rosa, M., Sadiq, S., Teniente, E. (eds.) CAiSE 2021. LNCS, vol. 12751, pp. 193–209. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79382-1_12

    Chapter  Google Scholar 

  8. Calvanese, D., Lanti, D., De Farias, T.M., Mosca, A., Xiao, G.: Accessing scientific data through knowledge graphs with Ontop. Patterns 2(10), 100346 (2021). https://doi.org/10.1016/j.patter.2021.100346

    Article  Google Scholar 

  9. Chacon, S., Straub, B.: Pro Git. Springer, Heidelberg (2014)

    Book  Google Scholar 

  10. Chen, Y.H., Lu, E.J.L., Ou, T.A.: Intelligent SPARQL query generation for natural language processing systems. IEEE Access 9, 158638–158650 (2021). https://doi.org/10.1109/ACCESS.2021.3130667

    Article  Google Scholar 

  11. Gousios, G.: The GHTorrent dataset and tool suite. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR 2013, Piscataway, NJ, USA, pp. 233–236. IEEE Press (2013). https://doi.org/10.5555/2487085.2487132

  12. Gousios, G., Spinellis, D.: GHTorrent: GitHub’s data from a firehose. In: 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp. 12–21. IEEE (2012). https://doi.org/10.1109/MSR.2012.6224294

  13. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  14. Gusenkov, A., Bukharaev, N., Birialtsev, E.: On ontology based data integration: problems and solutions. In: Journal of Physics: Conference Series, vol. 1203, p. 012059. IOP Publishing (2019). https://doi.org/10.1088/1742-6596/1203/1/012059

  15. Horridge, M., Jupp, S., Moulton, G., Rector, A., Stevens, R., Wroe, C.: A practical guide to building “OWL” ontologies using protégé 4 and co-ode tools edition1. 2. The University of Manchester, vol. 107 (2009)

    Google Scholar 

  16. Kharlamov, E., et al.: Ontology based data access in Statoil. J. Web Semant. 44, 3–36 (2017). https://doi.org/10.1016/j.websem.2017.05.005

    Article  Google Scholar 

  17. Kharlamov, E., et al.: Semantic access to streaming and static data at Siemens. J. Web Semant. 44, 54–74 (2017). https://doi.org/10.1016/j.websem.2017.02.001

    Article  Google Scholar 

  18. Kharlamov, E., et al.: Optique: ontology-based data access platform (2015)

    Google Scholar 

  19. Kubitza, D.O., Böckmann, M., Graux, D.: SemanGit: a linked dataset from git. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 215–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_14

    Chapter  Google Scholar 

  20. Lakzaei, B., Shamsfard, M.: Ontology learning from relational databases. Inf. Sci. 577, 280–297 (2021). https://doi.org/10.1016/j.ins.2021.06.074

    Article  MathSciNet  Google Scholar 

  21. Lenzerini, M., Daraio, C.: Challenges, approaches and solutions in data integration for research and innovation. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds.) Springer Handbook of Science and Technology Indicators. SH, pp. 397–420. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02511-3_15

    Chapter  Google Scholar 

  22. Liang, S., Stockinger, K., de Farias, T.M., Anisimova, M., Gil, M.: Querying knowledge graphs in natural language. J. Big Data 8(1), 1–23 (2021). https://doi.org/10.1186/s40537-020-00383-w

    Article  Google Scholar 

  23. Liao, C., Wu, Y., King, G.: Research on learning OWL ontology from relational database. In: Journal of Physics: Conference Series, vol. 1176, p. 022031. IOP Publishing (2019). https://doi.org/10.1088/1742-6596/1176/2/022031

  24. Lohmann, S., Link, V., Marbach, E., Negru, S.: WebVOWL: web-based visualization of ontologies. In: Lambrix, P., et al. (eds.) EKAW 2014. LNCS (LNAI), vol. 8982, pp. 154–158. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17966-7_21

    Chapter  Google Scholar 

  25. Ma, C., Molnár, B.: Use of ontology learning in information system integration: a literature survey. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds.) ACIIDS 2020. CCIS, vol. 1178, pp. 342–353. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3380-8_30

    Chapter  Google Scholar 

  26. El Massari, H., Mhammedi, S., Gherabi, N., Nasri, M.: Virtual OBDA mechanism Ontop for answering SPARQL queries over Couchbase. In: Saidi, R., El Bhiri, B., Maleh, Y., Mosallam, A., Essaaidi, M. (eds.) ICATH 2021. LNDECT, vol. 110, pp. 193–205. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94188-8_19

    Chapter  Google Scholar 

  27. Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: a guide to creating your first ontology (2001)

    Google Scholar 

  28. Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 133–173. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77688-8_5

    Chapter  MATH  Google Scholar 

  29. Priyatna, F., Corcho, O., Sequeda, J.: Formalisation and experiences of R2RML-based SPARQL to SQL query translation using Morph. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 479–490 (2014). https://doi.org/10.1145/2566486.2567981

  30. Sequeda, J.F., Miranker, D.P.: Ultrawrap: SPARQL execution on relational data. J. Web Semant. 22, 19–39 (2013). https://doi.org/10.1016/j.websem.2013.08.002

    Article  Google Scholar 

  31. Staab, S., Studer, R. (eds.): Handbook on Ontologies. IHIS, Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3

    Book  MATH  Google Scholar 

  32. Xiao, G., et al.: Ontology-based data access: a survey. In: International Joint Conferences on Artificial Intelligence (2018). https://doi.org/10.24963/ijcai.2018/777

  33. Xiao, G., Ding, L., Cogrel, B., Calvanese, D.: Virtual knowledge graphs: an overview of systems and use cases. Data Intell. 1(3), 201–223 (2019). https://doi.org/10.1162/dint_a_00011

  34. Xiao, G., et al.: The virtual knowledge graph system Ontop. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 259–277. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_17

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of the Western Cape, Cape Town, South Africa

    Yahlieel Jafta & Louise Leenen

  2. University of Cape Town, Cape Town, South Africa

    Thomas Meyer

  3. Centre for Artificial Intelligence Research (CAIR), Cape Town, South Africa

    Yahlieel Jafta, Louise Leenen & Thomas Meyer

Authors
  1. Yahlieel Jafta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louise Leenen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yahlieel Jafta or Louise Leenen .

Editor information

Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jafta, Y., Leenen, L., Meyer, T. (2023). Investigating Ontology-Based Data Access with GitHub. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-33455-9_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33454-2

  • Online ISBN: 978-3-031-33455-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation