Abstract
Semantic web technologies have proved their usefulness in facilitating the documentation, annotation and to a certain extent the reuse and reproducibility of scientific experiments in laboratories. While useful, existing solutions suffer from some limitations when it comes to supporting scientists. Indeed, it is up to him/her to identify which ontologies to use, which fragments of those ontologies are useful for his experiments and how to combine them. Furthermore, the behavior and constraints of the domain of interest to the scientist, e.g., constraints and business rules, are not captured and as such are decoupled from the ontologies. To overcome the above limitations, we propose in this paper a methodology and underlying ontologies and solutions with the view to facilitate for the scientist the task of creating an ontology that captures the specificities of the domain of interest by combining existing well-known ontologies. Moreover, we provide the scientist with the means of specifying behavioral constraints, such as integrity constraints and business rules, with the ontology specified. We showcase our solution using a real-world case study from the field of agronomy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
AFO Ontologies: https://www.allotrope.org/ontologies.
- 2.
FOAF Ontology : http://www.foaf-project.org.
- 3.
vCard Ontology: https://www.w3.org/TR/vcard-rdf/.
- 4.
- 5.
ENVO Ontology: https://bioportal.bioontology.org/ontologies/ENVO.
- 6.
OBO Foundry: http://www.obofoundry.org.
- 7.
Scientific Experiments Core Ontology: https://github.com/aloulen/SECO.
- 8.
CC BY 4.0: http://creativecommons.org/licenses/by/4.0/.
- 9.
PATO Ontology: http://www.obofoundry.org/ontology/pato.html.
- 10.
QUDT Ontologies: http://www.qudt.org/release2/qudt-catalog.html.
- 11.
Scientific Experiments Agronomy Ontology: https://github.com/aloulen/SECO_AGRO.
- 12.
AgiLab is a software development company that provides information systems (LIMS) for managing the activities of research and development laboratories in different domains of activity.
- 13.
eSciDoc : https://www.escidoc.org.
- 14.
OBI Ontology: http://obi-ontology.org.
- 15.
Allotrope Foundation : https://www.allotrope.org/about-us.
- 16.
OBO Foundry best practices: http://www.obofoundry.org/principles/fp-000-summary.html.
References
Arp, R., Smith, B., Spear, A.D.: Building Ontologies with Basic Formal Ontology. MIT Press, Cambridge (2015)
Brahaj, A., Razum, M., Schwichtenberg, F.: Ontological formalization of scientific experiments based on core scientific metadata model. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds.) TPDL 2012. LNCS, vol. 7489, pp. 273–279. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33290-6_29
Brinkman, R.R., et al.: Modeling biomedical experimental processes with OBI. J. Biomed. Semant. 1, S7 (2010). BioMed Central
Ciccarese, P., Ocana, M., Castro, L.J.G., Das, S., Clark, T.: An open annotation ontology for science on web 3.0. J. Biomed. Semant. 2, S4 (2011). BioMed Central
Das, S.: R2RML: RDB TO RDF mapping language (2011). http://www.w3.org/TR/r2rml/
Degtyarenko, K., et al.: ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 36(suppl–1), D344–D350 (2007)
Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)
Fritzsche, D., et al.: Ontology summit 2016 communique: ontologies within semantic interoperability ecosystems. Appl. Ontol. 12(2), 91–111 (2017)
González-Beltrán, A., Maguire, E., Sansone, S.A., Rocca-Serra, P.: linkedISA: semantic representation of ISA-TAB experimental metadata. BMC Bioinf. 15(14), S4 (2014)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum Comput Stud. 43(5–6), 907–928 (1995)
Gyrard, A., Serrano, M., Atemezing, G.A.: Semantic web methodologies, best practices and ontology engineering applied to Internet of Things. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 412–417. IEEE (2015)
Haag, F., Lohmann, S., Siek, S., Ertl, T.: QueryVOWL: a visual query notation for linked data. In: Gandon, F., Guéret, C., Villata, S., Breslin, J., Faron-Zucker, C., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 387–402. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25639-9_51
Harris, S., Seaborne, A., Prud’hommeaux, E.: SPARQL 1.1 query language. W3C Recomm. 21(10), 778 (2013)
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M., et al.: SWRL: a semantic web rule language combining owl and RuleML. W3C Memb. Submiss. 21, 79 (2004)
Lassila, O., Swick, R.R.: Resource description framework (RDF) model and syntax specification (1999)
Vazquez-Naya, J.M., et al.: Ontologies of drug discovery and design for neurology, cardiology and oncology. Curr. Pharm. Des. 16(24), 2724–2736 (2010)
Rocca-Serra, P., et al.: ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26(18), 2354–2356 (2010)
Simperl, E.: Reusing ontologies on the semantic web: a feasibility study. Data Knowl. Eng. 68(10), 905–925 (2009)
Smith, B., et al.: The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25(11), 1251 (2007)
Soldatova, L.N., King, R.D.: An ontology of scientific experiments. J. R. Soc. Interface 3(11), 795–803 (2006)
Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: OptiqueVQS: towards an ontology-based visual query system for big data. In: Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems, pp. 119–126. ACM (2013)
Stoeckert, C.J., Parkinson, H.: The MGED ontology: a framework for describing functional genomics experiments. Int. J. Genomics 4(1), 127–132 (2003)
Vandenbussche, P.Y., Vatant, B.: Metadata recommendations for linked open data vocabularies. Version 1, 2011–2012 (2011)
Visser, U., Abeyruwan, S., Vempati, U., Smith, R.P., Lemmon, V., Schürer, S.C.: Bioassay ontology (BAO): a semantic description of bioassays and high-throughput screening results. BMC Bioinf. 12(1), 257 (2011)
Wilkinson, M.D., et al.: The fair guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Aloulen, Z., Belhajjame, K., Grigori, D., Acker, R. (2019). A Domain-Independent Ontology for Capturing Scientific Experiments. In: Kotzinos, D., Laurent, D., Spyratos, N., Tanaka, Y., Taniguchi, Ri. (eds) Information Search, Integration, and Personalization. ISIP 2018. Communications in Computer and Information Science, vol 1040. Springer, Cham. https://doi.org/10.1007/978-3-030-30284-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-30284-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30283-2
Online ISBN: 978-3-030-30284-9
eBook Packages: Computer ScienceComputer Science (R0)