Skip to main content
SpringerLink
Log in

A Framework for Automatic Knowledge Base Generation from Observation Data Sets

  • Conference paper
  • First Online:
Current Trends in Web Engineering (ICWE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1898))

Included in the following conference series:

  • 97 Accesses

Abstract

In the Semantic Web of Everything, observation data collected from sensors and devices disseminated in smart environments must be annotated in order to produce a Knowledge Base (KB) or Knowledge Graph (KG) which can be used subsequently for inference. Available approaches allow defining complex data models for mapping tabular data to KBs/KGs: while granting high flexibility, they can be difficult to use. This paper introduces a framework for automatic KB generation in Web Ontology Language (OWL) 2 from observation data sets. It aims at simplicity both in usage and in expressiveness of generated KBs, in order to enable reasoning with SWoE inference engines in pervasive and embedded devices. An illustrative example from a precision farming case study clarifies the approach and early performance results support its computational sustainability.

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 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
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.

    https://saref.etsi.org/saref4agri/.

References

  1. Abdelmageed, N., Schindler, S.: JenTab: matching tabular data to knowledge graphs. In: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Workshop, International Semantic Web Conference, 40–49 (2020)

    Google Scholar 

  2. Ahmed, M., Seraj, R., Islam, S.M.S.: The K-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020)

    Article  Google Scholar 

  3. Arenas-Guerrero, J., Chaves-Fraga, D., Toledo, J., Pérez, M.S., Corcho, O.: Morph-KGC: Scalable knowledge graph materialization with mapping partitions. Semantic Web, pp. 1–20 (2022)

    Google Scholar 

  4. Baader, F., Calvanese, D., McGuinness, D., Patel-Schneider, P., Nardi, D., et al.: The Description Logic Handbook: Theory. Implementation and Applications. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  5. Bray, T.: RFC 8259: The JavaScript object notation (JSON) data interchange format (2017)

    Google Scholar 

  6. Chiew, K.L., Tan, C.L., Wong, K., Yong, K.S., Tiong, W.K.: A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf. Sci. 484(C), 153–166 (2019)

    Article  Google Scholar 

  7. Daniele, L., den Hartog, F., Roes, J.: Created in close interaction with the industry: the smart appliances REFerence (SAREF) ontology. In: Cuel, R., Young, R. (eds.) FOMI 2015. LNBIP, vol. 225, pp. 100–112. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21545-7_9

    Chapter  Google Scholar 

  8. Das, S., Sundara, S., Cyganiak, R.: R2RML: RDB to RDF Mapping Language. Recommendation, W3C (2012). https://www.w3.org/TR/r2rml/

  9. De Una, D., Rümmele, N., Gange, G., Schachte, P., Stuckey, P.J.: Machine learning and constraint programming for relational-to-ontology schema mapping. In: International Joint Conference on Artificial Intelligence, pp. 1277–1283 (2018)

    Google Scholar 

  10. Delva, T., Arenas-Guerrero, J., Iglesias-Molina, A., Corcho, O., Chaves-Fraga, D., Dimou, A.: RML-star: a declarative mapping language for RDF-star generation. In: ISWC2021, the International Semantic Web Conference, vol. 2980. CEUR (2021)

    Google Scholar 

  11. 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: Workshop on Linked Data on the Web, 23rd International World Wide Web Conference, pp. 1–5 (2014)

    Google Scholar 

  12. Guha, R.V., Brickley, D., Macbeth, S.: Schema Org.: evolution of structured data on the web. Commun. ACM 59(2), 44–51 (2016)

    Article  Google Scholar 

  13. Horridge, M., Bechhofer, S.: The OWL API: a java API for OWL ontologies. Semant. Web 2(1), 11–21 (2011)

    Article  Google Scholar 

  14. Horridge, M., Patel-Schneider, P.: OWL 2 Web Ontology Language Manchester Syntax (Second Edition). W3C note, W3C (2012). https://www.w3.org/TR/owl2-manchester-syntax/

  15. Janowicz, K., Haller, A., Cox, S.J., Le Phuoc, D., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 56, 1–10 (2019)

    Article  Google Scholar 

  16. Jiménez-Ruiz, E., et al.: BootOX: practical mapping of RDBs to OWL 2. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 113–132. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25010-6_7

    Chapter  Google Scholar 

  17. Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. web 6(2), 167–195 (2015)

    Article  Google Scholar 

  18. Liu, J., Chabot, Y., Troncy, R., Huynh, V.P., Labbé, T., Monnin, P.: From tabular data to knowledge graphs: a survey of semantic table interpretation tasks and methods. J. Web Semant. 76, 100761 (2022)

    Article  Google Scholar 

  19. Liu, J., Troncy, R.: Dagobah: an end-to-end context-free tabular data semantic annotation system. In: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Workshop, International Semantic Web Conference, pp. 41–48 (2019)

    Google Scholar 

  20. Modoni, G.E., Sacco, M.: Discovering critical factors affecting RDF stores success. In: Pandey, R., Paprzycki, M., Srivastava, N., Bhalla, S., Wasielewska-Michniewska, K. (eds.) Semantic IoT: Theory and Applications. SCI, vol. 941, pp. 193–206. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64619-6_8

    Chapter  Google Scholar 

  21. O’Connor, M.J., Halaschek-Wiener, C., Musen, M.A.: Mapping master: a flexible approach for mapping spreadsheets to OWL. In: Patel-Schneider, P.F., et al. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 194–208. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17749-1_13

    Chapter  Google Scholar 

  22. Parsia, B., Rudolph, S., Krötzsch, M., Patel-Schneider, P., Hitzler, P.: OWL 2 Web Ontology Language Primer (Second Edition). W3C Recommendation, W3C (2012). https://www.w3.org/TR/owl2-primer

  23. Pinkel, C., et al.: RODI: benchmarking relational-to-ontology mapping generation quality. Semant. Web 9(1), 25–52 (2018)

    Article  Google Scholar 

  24. Ruta, M., et al.: A multiplatform reasoning engine for the semantic web of everything. J. Web Semant. 73, 100709 (2022)

    Article  Google Scholar 

  25. Ruta, M., Scioscia, F., Loseto, G., Pinto, A., Di Sciascio, E.: Machine learning in the internet of things: a semantic-enhanced approach. Semant. Web 10(1), 183–204 (2019)

    Article  Google Scholar 

  26. Schreiber, G., Gandon, F.: RDF 1.1 XML syntax. Recommendation, W3C (2014). https://www.w3.org/TR/rdf-syntax-grammar/

  27. Schreiber, G., Gandon, F.: RDF-star and SPARQL-star. Draft community group report, W3C (2023). https://w3c.github.io/rdf-star/cg-spec/editors_draft.html

  28. Talburt, J.R., Ehrlinger, L., Magruder, J.: Automated data curation and data governance automation. Front. Big Data 6, 1148331 (2023)

    Article  Google Scholar 

  29. Thorndike, R.: Who belongs in the family? Psychometrika 18(4), 267–276 (1953)

    Article  Google Scholar 

  30. Van Veen, T.: Wikidata: from “an’’ identifier to “the’’ identifier. Inf. Technol. Libr. 38(2), 72–81 (2019)

    Google Scholar 

  31. Vu, B., Knoblock, C., Pujara, J.: Learning semantic models of data sources using probabilistic graphical models. In: The World Wide Web Conference, pp. 1944–1953 (2019)

    Google Scholar 

Download references

Acknowledgments

This work has been supported by project TEBAKA (TErritorial BAsic Knowledge Acquisition), funded by the Italian Ministry of University and Research.

Author information

Authors and Affiliations

  1. Polytechnic University of Bari, 70125, Bari, Italy

    Agnese Pinto, Saverio Ieva, Arnaldo Tomasino, Floriano Scioscia, Michele Ruta & Francesco De Feudis

  2. LUM University “Giuseppe Degennaro”, 70010, Casamassima, BA, Italy

    Giuseppe Loseto

Authors
  1. Agnese Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saverio Ieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arnaldo Tomasino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Loseto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Floriano Scioscia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michele Ruta
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Francesco De Feudis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Floriano Scioscia .

Editor information

Editors and Affiliations

  1. Jaume I University, Castellón de la Plana, Spain

    Sven Casteleyn

  2. University of Jyväskylä, Jyväskylä, Finland

    Tommi Mikkonen

  3. Universitat Politècnica de València, Valencia, Spain

    Alberto García Simón

  4. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    In-Young Ko

  5. LUM “Giuseppe Degennaro” University, Casamassima, Italy

    Giuseppe Loseto

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Pinto, A. et al. (2024). A Framework for Automatic Knowledge Base Generation from Observation Data Sets. In: Casteleyn, S., Mikkonen, T., García Simón, A., Ko, IY., Loseto, G. (eds) Current Trends in Web Engineering. ICWE 2023. Communications in Computer and Information Science, vol 1898. Springer, Cham. https://doi.org/10.1007/978-3-031-50385-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50385-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50384-9

  • Online ISBN: 978-3-031-50385-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation