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SHACL Shapes Generation from Textual Documents

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Enterprise and Organizational Modeling and Simulation (EOMAS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 366))

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Abstract

Shapes Constraint Language (SHACL) is the new recommendation by W3C consortium to uniform both describing and constraining the content of an RDF graph. Based on the inspiration of model generation from textual requirements specifications, we investigate the possibility of mapping parts of a textual document to shapes described by SHACL. In this contribution, we present our approach of the patterns (based on a grammatical inspection) that indicates candidates of domain description in SHACL language. We argue that the standard methods of linguistics can be supported by ontology resources as Schema.org.

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Notes

  1. 1.

    https://wiki.dbpedia.org/.

  2. 2.

    Terminated project – data still available via https://developers.google.com/freebase.

  3. 3.

    https://schema.org.

  4. 4.

    http://www.conceptnet.io.

  5. 5.

    https://schema.org.

References

  1. SHACL and OWL Compared. http://spinrdf.org/shacl-and-owl.html. Accessed 08 Jan 2019

  2. Arellano, A., Zontek-Carney, E., Austin, M.A.: Frameworks for natural language processing of textual requirements. Int. J. Adv. Syst. Meas. 8, 230–240 (2015)

    Google Scholar 

  3. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  4. Ben Abdessalem Karaa, W., Ben Azzouz, Z., Singh, A., Dey, N., Ashour, A.S., Ben Ghazala, H.: Automatic builder of class diagram (ABCD): an application of UML generation from functional requirements. Softw. Pract. Exp. 46(11), 1443–1458 (2016)

    Google Scholar 

  5. Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts and Abstract Syntax. W3C recommendation, W3C (2014). http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225

  6. Draicchio, F., Gangemi, A., Presutti, V., Nuzzolese, A.G.: FRED: from natural language text to RDF and OWL in one click. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 263–267. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_36

    Chapter  Google Scholar 

  7. Gardent, C., Shimorina, A., Narayan, S., Perez-Beltrachini, L.: The WebNLG challenge: generating text from RDF data. In: Proceedings of the 10th International Conference on Natural Language Generation, Spain, pp. 124–133. Association for Computational Linguistics, Santiago de Compostela (2017)

    Google Scholar 

  8. Guha, R., Brickley, D.: RDF Schema 1.1. W3C recommendation, W3C (2014). http://www.w3.org/TR/2014/REC-rdf-schema-20140225/

  9. Irene, P.: From OWL to SHACL in an automated way. https://www.topquadrant.com/2018/05/01/from-owl-to-shacl-in-an-automated-way (2018). Accessed 19 May 2019

  10. Landhäußer, M., Körner, S.J., Tichy, W.F.: From requirements to UML models and back: how automatic processing of text can support requirements engineering. Softw. Qual. J. 22(1), 121–149 (2014)

    Article  Google Scholar 

  11. Martinez-Rodriguez, J.L., Lopez-Arevalo, I., Rios-Alvarado, A.B., Hernandez, J., Aldana-Bobadilla, E.: Extraction of RDF statements from text. In: Villazón-Terrazas, B., Hidalgo-Delgado, Y. (eds.) KGSWC 2019. CCIS, vol. 1029, pp. 87–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21395-4_7

    Chapter  Google Scholar 

  12. McGuinness, D., van Harmelen, F.: OWL Web Ontology Language Overview. W3C recommendation, W3C (2004). http://www.w3.org/TR/2004/REC-owl-features-20040210

  13. Mylopoulos, J.: Conceptual Modelling and Telos. Conceptual Modelling, Databases and CASE: An Integrated View of Information System Development. Wiley, New York (1992)

    Google Scholar 

  14. Pandit, H.J., O’Sullivan, D., Lewis, D.: Using ontology design patterns to define SHACL shapes. In: Proceedings of the 9th Workshop on Ontology Design and Patterns (WOP 2018) Co-located with 17th International Semantic Web Conference (ISWC 2018), pp. 67–71 (2018)

    Google Scholar 

  15. Perera, R., Nand, P., Klette, G.: RealText-lex: a lexicalization framework for RDF triples. Prague Bull. Math. Linguist. 106(1), 45–68 (2016)

    Article  Google Scholar 

  16. Šenkýř, D., Kroha, P.: Patterns in textual requirements specification. In: Proceedings of the 13th International Conference on Software Technologies, Porto, Portugal, pp. 197–204. SCITEPRESS - Science and Technology Publications (2018)

    Google Scholar 

  17. Speer, R., Havasi, C.: Representing General Relational Knowledge in ConceptNet 5. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-2012). European Language Resources Association (ELRA) (2012)

    Google Scholar 

  18. Staab, S., Lehmann, J., Verborgh, R.: Structured knowledge on the Web 7.0. In: Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW 2018, Lyon, France, pp. 885–886. ACM Press (2018)

    Google Scholar 

  19. Taylor, A., Marcus, M., Santorini, B.: The Penn Treebank: an overview. In: Abeillé, A. (ed.) Treebanks. Text, Speech and Language Technolog, vol. 20, pp. 5–22. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-010-0201-1_1

    Chapter  Google Scholar 

  20. Trisedya, B.D., Qi, J., Zhang, R., Wang, W.: GTR-LSTM: a triple encoder for sentence generation from RDF data. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, vol. 1, pp. 1627–1637. Association for Computational Linguistics (2018)

    Google Scholar 

  21. Verdonck, M., Gailly, F., Pergl, R., Guizzardi, G., Martins, B., Pastor, O.: Comparing traditional conceptual modeling with ontology-driven conceptual modeling: an empirical study. Inf. Syst. 81, 92–103 (2019)

    Article  Google Scholar 

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Acknowledgement

This research was supported by the grant of Czech Technical University in Prague No. SGS17/211/OHK3/3T/18.

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Authors and Affiliations

  1. Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic

    David Šenkýř

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  1. David Šenkýř
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Correspondence to David Šenkýř .

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Editors and Affiliations

  1. Czech Technical University in Prague, Prague 6, Czech Republic

    Robert Pergl

  2. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Eduard Babkin

  3. Loughborough University, Loughborough, UK

    Russell Lock

  4. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Pavel Malyzhenkov

  5. Czech Technical University in Prague, Prague 1, Czech Republic

    Vojtěch Merunka

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Šenkýř, D. (2019). SHACL Shapes Generation from Textual Documents. In: Pergl, R., Babkin, E., Lock, R., Malyzhenkov, P., Merunka, V. (eds) Enterprise and Organizational Modeling and Simulation. EOMAS 2019. Lecture Notes in Business Information Processing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-030-35646-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-35646-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35645-3

  • Online ISBN: 978-3-030-35646-0

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