Advertisement

PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies

Chapter
  • 97 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 941)

Abstract

Sensor-based applications are increasingly present in our everyday life. Due to the enormous quantity of sensor data produced, interpreting data and building interoperable sensor-based applications is needed. There are several problems to address the heterogeneity of (1) data format, (2) languages to describe sensor metadata, (3) models for structuring sensor datasets, (4) reasoning mechanisms and rule languages to interpret sensor datasets, and (5) applications. Semantic Web technologies (a.k.a, knowledge graphs), are immersed in an increasing number of online activities we perform today (e.g., search engines for gathering information). There is a need to find better ways to share data and distribute more meaningful and more accurate information. Innovative methodologies are needed to link and associate the data from different domains to improve knowledge discovery. Semantic knowledge graphs, made of datasets and ontologies, are intended to describe and organize heterogeneous data explicitly. If an ontology is widely used to structure data of a particular domain, the accessibility and the efficiency in sharing and reusing that information will increase. For this reason, we focused on the ontology quality used when building sensor-based applications. We designed PerfectO, a Knowledge Directory Services tool, focusing on ontology best practices, which: (1) improves knowledge quality, (2) leverages usability, accessibility, and classification of the information, (3) enhances engineering experience, and (4) promotes engineering best practices. PerfectO implementation is applied to the Internet of Things (IoT) domain because it covers more than 20 application domains (e.g., healthcare, smart building, smart farm) that use sensors. PerfectO enhances knowledge expertise quality implemented within any ontologies as demonstrated with the Linked Open Vocabularies for IoT (LOV4IoT) ontology catalog.

Keywords

Knowledge directory Knowledge directory service Semantic data interoperability Ontology quality Methodology Web of things Internet of things Semantic web of things Semantic web technologies 

Notes

Acknowledgements

This work has partially received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857237 (Interconnect), Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, Insight Centre for Data Analytics and H2020 FIESTA-IoT-CNECT-ICT-643943. The opinions expressed are those of the authors and do not reflect those of the sponsors.

References

  1. 1.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)Google Scholar
  2. 2.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Int. J. Semant. Web Inf. Syst. (2009)Google Scholar
  3. 3.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum.-Comput. Stud. (1995)Google Scholar
  4. 4.
    Vandenbussche, P.Y., Atemezing, G.A., Poveda-Villalón, M., Vatant, B.: Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web. Semant. Web J. (2016)Google Scholar
  5. 5.
    Gyrard, A., Zimmermann, A., Sheth, A.: Building IoT based applications for Smart Cities: how can ontology catalogs help? IEEE Internet Things J. (2018)Google Scholar
  6. 6.
    Gyrard, A., Bonnet, C., Boudaoud, K., Serrano, M.: LOV4IoT: a second life for ontology-based domain knowledge to build Semantic Web of Things applications. In: IEEE International Conference on Future Internet of Things and Cloud (2016)Google Scholar
  7. 7.
    Gyrard, A., Atemezing, G., Bonnet, C., Boudaoud, K., Serrano, M.: Reusing and unifying background knowledge for internet of things with LOV4IoT. In: IEEE International Conference on Future Internet of Things and Cloud (2016)Google Scholar
  8. 8.
    Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semant. Web J. 7(1):63–93 (2015)Google Scholar
  9. 9.
    McDaniel, M., Storey, V.C., Sugumaran, V.: Assessing the quality of domain ontologies: metrics and an automated ranking system. Data Knowl. Eng. 115, 32–47 (2018)Google Scholar
  10. 10.
    Raad, J., Cruz, C.: A survey on ontology evaluation methods. In: KEOD (2015)Google Scholar
  11. 11.
    Hlomani, H., Stacey, D.: Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: a survey. Semant. Web J. (2014)Google Scholar
  12. 12.
    García, J., Jose’García-Peñalvo, F., Therón, R.: A survey on ontology metrics. In: World Summit on Knowledge Society. Springer (2010)Google Scholar
  13. 13.
    Fernández-López, M., Poveda-Villalón, M., Suárez-Figueroa, M.C., Gómez-Pérez, A.: Why are ontologies not reused across the same domain? J. Web Semant. (2018)Google Scholar
  14. 14.
    Rus, I., Lindvall, M.: Knowledge management in software engineering. IEEE Softw. J. 19, 26–38 (2002)Google Scholar
  15. 15.
    Serrano, M., Barnaghi, P., Carrez, F., Cousin, P., Vermesan, O., Friess, P.: Internet of Things IoT Semantic Interoperability: Research Challenges, Best Practices, Recommendations and Next Steps. Technical report, IERC AC4 (2015)Google Scholar
  16. 16.
    Agarwal, R., Fernandez, D.G., Elsaleh, T., Gyrard, A., Lanza, J., Sanchez, L., Georgantas, N., Issarny, V.: Unified IoT ontology to enable interoperability and federation of testbeds. In: IEEE World Forum on Internet of Things (2016)Google Scholar
  17. 17.
    FIESTA IoT Consortium, E.: FIESTA-IoT project Deliverable 6.1 Design of Global Market Confidence Programme on IoT interoperability (2016)Google Scholar
  18. 18.
    Gyrard, A., Serrano, M., Atemezing, G.: Semantic web methodologies, best practices and ontology engineering applied to internet of things. In: IEEE World Forum on Internet of Things (2015)Google Scholar
  19. 19.
    Suárez-Figueroa, M.C.: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse. PhD thesis, Universidad Politecnica de Madrid, Facultad de Informatica, Departamento de Inteligencia Artificial (2010)Google Scholar
  20. 20.
    Noy, N.F., McGuinness, D.L.: Ontology Development 101: A Guide to Creating your First Ontology (2001)Google Scholar
  21. 21.
    Zaslavsky, A., Perera, C., Georgakopoulos, D.: Sensing as a Service and Big Data. arXiv preprint arXiv:1301.0159 (2013)
  22. 22.
    Gyrard, A., Serrano, M.: Connected Smart Cities: interoperability with SEG 3.0 for the Internet of Things. In: 30th IEEE International Conference on Advanced Information Networking and Applications Workshops (2016)Google Scholar
  23. 23.
    Rezaei, R., Chiew, T.K., Lee, S.P., Aliee, Z.S.: Interoperability evaluation models: a systematic review. Comput. Ind. (2014)Google Scholar
  24. 24.
    Serrano, M., Barnaghi, P., Cousin, P.: Semantic Interoperability: Research Challenges, Best Practices, Solutions and Next Steps, IERC AC4 Manifesto. Technical report, European Research Cluster on the Internet of Things, AC4 (2014)Google Scholar
  25. 25.
    Gyrard, A., Bonnet, C.: Semantic Web best practices: Semantic Web Guidelines for domain knowledge interoperability to build the Semantic Web of Things. OneM2M International Standard, Management, Abstraction and Semantics (MAS) Working Group 5, April 2014, Eurecom (2014)Google Scholar
  26. 26.
    Murdock, P., Bassbouss, L., Bauer, M., Alaya, M.B., Bhowmik, R., Brett, P., Chakraborty, R.N., Dadas, M., Davies, J., Diab, W., et al.: Semantic Interoperability for the Web of Things (2016)Google Scholar
  27. 27.
    Bauer, M., Baqa, H., Bilbao, S., Corchero, A., Daniele, L., Esnaola, I., Fernandez, I., Franberg, O., Garcia-Castro, R., Girod-Genet, M., Guillemin, P., Gyrard, A., Kaed, C.E., Kung, A., Lee, J., Lefrançois, M., Li, W., Raggett, D., Wetterwald, M.: Semantic IoT Solutions—A Developer Perspective (Semantic Interoperability White Paper Part I) (2019)Google Scholar
  28. 28.
    Bauer, M., Baqa, H., Bilbao, S., Corchero, A., Daniele, L., Esnaola, I., Fernandez, I., Franberg, O., Garcia-Castro, R., Girod-Genet, M., Guillemin, P., Gyrard, A., Kaed, C.E., Kung, A., Lee, J., Lefrançois, M., Li, W., Raggett, D., Wetterwald, M.: Towards semantic interoperability standards based on ontologies (Semantic Interoperability White Paper Part II) (2019)Google Scholar
  29. 29.
    Grüninger, M., Fox, M.S.: Methodology for the Design and Evaluation of Ontologies (1995)Google Scholar
  30. 30.
    Ma, X., Fu, L., West, P., Fox, P.: Ontology usability scale: context-aware metrics for the effectiveness, efficiency and satisfaction of ontology uses. Data Sci. J. (2018)Google Scholar
  31. 31.
    Corcho, O., Fernández-López, M., Gómez-Pérez, A.: Methodologies, tools and languages for building ontologies. Where is their meeting point? Data Knowl. Eng. J. 46, 41–64 (2003)Google Scholar
  32. 32.
    Suarez-Figueroa, M.C., Gomez-Perez, A., Fernandez-Lopez, M.: The NeOn methodology for ontology engineering. In: Ontology Engineering in a Networked World. Springer (2012)Google Scholar
  33. 33.
    Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2013)Google Scholar
  34. 34.
    Fernández-López, M., Gómez-Pérez, A., Juristo, N.: Methontology: From Ontological Art Towards Ontological Engineering (1997)Google Scholar
  35. 35.
    Hitzler, P., Gangemi, A., Janowicz, K.: Ontology Engineering with Ontology Design Patterns: Foundations and Applications. IOS Press (2016)Google Scholar
  36. 36.
    Poveda-Villalón, M., Gómez-Pérez, A., Suárez-Figueroa, M.C.: OOPS!(Ontology Pitfall Scanner!): an on-line tool for ontology evaluation. Int. J. Semant. Web Inf. Syst. (2014)Google Scholar
  37. 37.
    Duque-Ramos, A., Fernández-Breis, J.T., Iniesta, M., Dumontier, M., Aranguren, M.E., Schulz, S., Aussenac-Gilles, N., Stevens, R.: Evaluation of the oquare framework for ontology quality. Expert Syst. Appl. (2013)Google Scholar
  38. 38.
    Duque-Ramos, A., Fernández-Breis, J.T., Stevens, R., Aussenac-Gilles, N.: OQuaRE: a SQuaRE-based approach for evaluating the quality of ontologies. J. Res. Pract. Inf. Technol. (H Index=21) (2011)Google Scholar
  39. 39.
    Fernández, M., Overbeeke, C., Sabou, M., Motta, E.: What makes a good ontology? A case-study in fine-grained knowledge reuse. In: Asian Conference on The Semantic Web. Springer (2009)Google Scholar
  40. 40.
    Tartir, S., Arpinar, I.B.: Ontology evaluation and ranking using OntoQA. In: Semantic Computing, 2007. ICSC 2007. International Conference on. IEEE (2007)Google Scholar
  41. 41.
    Tartir, S., Arpinar, I.B., Moore, M., Sheth, A.P., Aleman-Meza, B.: OntoQA: Metric-Based Ontology Quality Analysis (2005)Google Scholar
  42. 42.
    Brank, J., Grobelnik, M., Mladenić, D.: A Survey of Ontology Evaluation Techniques (2005)Google Scholar
  43. 43.
    Burton-Jones, A., Storey, V.C., Sugumaran, V., Ahluwalia, P.: A semiotic metrics suite for assessing the quality of ontologies. Data Knowl. Eng. (2005)Google Scholar
  44. 44.
    Lozano-Tello, A., Gómez-Pérez, A.: OntoMetric: a method to choose the appropriate ontology. J. Database Manag.(2004)Google Scholar
  45. 45.
    Vrandečić, D.: Ontology evaluation. In: Handbook on Ontologies. Springer (2009)Google Scholar
  46. 46.
    Vrandečić, D., Gangemi, A.: Unit tests for ontologies. In: On the Move to Meaningful Internet Systems OTM Workshops. Springer (2006)Google Scholar
  47. 47.
    Gangemi, A., Presutti, V.: Ontology design patterns. In: Handbook on Ontologies. Springer (2009)Google Scholar
  48. 48.
    Bezerra, C., Freitas, F., Euzenat, J., Zimmermann, A.: ModOnto: a tool for modularizing ontologies. In: Proceedings of 3rd Workshop on ontologies and Their Applications (Wonto) (2008)Google Scholar
  49. 49.
    Garijo, D.: WIDOCO: a Wizard for Documenting Ontologies. In: International Semantic Web Conference (ISWC, A-rank Conference). Springer (2017)Google Scholar
  50. 50.
    Fielding, R.T., Taylor, R.N.: Principled design of the modern web architecture. ACM Trans. Internet Technol. (TOIT) (2002)Google Scholar
  51. 51.
    Kolbe, N., Kubler, S., Le Traon, Y.: Popularity-driven ontology ranking using qualitative features. In: International Semantic Web Conference. Springer (2019)Google Scholar
  52. 52.
    Olivares-Alarcos, A., Beßler, D., Khamis, A., Goncalves, P., Habib, M.K., Bermejo, J., Barreto, M., Diab, M., Rosell, J., Quintas, J., Olszewska, J., Nakawala, H., Pignaton, E., Gyrard, A., Borgo, S., Alenya, G., Beetz, M., Li, H.: A Review and Comparison of Ontology-Based Approaches to Robot Autonomy (2019)Google Scholar
  53. 53.
    Gyrard, A., Sheth, A.: IAMHAPPY: Towards An IoT Knowledge-Based Cross-Domain Well-Being Recommendation System for Everyday Happiness (2019)Google Scholar
  54. 54.
    Lecue, F., Tamma, V.: ISWC 2017 Resources Track: Author and Reviewer Instructions (2017)Google Scholar
  55. 55.
    Buzan, T., Buzan, B.: The Mind Map Book: How to Use Radiant Thinking to Maximize Your Brain’s Untapped Potential (1996)Google Scholar
  56. 56.
    McBride, B.: Jena: a semantic web toolkit. Internet Comput. 6, 55–59 (2002)Google Scholar
  57. 57.
    Tejo-Alonso, C., Berrueta, D., Polo, L., Fernández, S.: Metadata for web ontologies and rules: current practices and perspectives. In: Metadata and Semantic Research. Springer (2011)Google Scholar
  58. 58.
    Peroni, S., Shotton, D., Vitali, F.: Tools for the automatic generation of ontology documentation: a task-based evaluation. In: Computational Linguistics: Concepts, Methodologies, Tools, and Applications. IGI Global (2014)Google Scholar
  59. 59.
    Lohmann, S., Link, V., Marbach, E., Negru, S.: WebVOWL: Web-based visualization of ontologies. In: Knowledge Engineering and Knowledge Management. Springer (2014)Google Scholar
  60. 60.
    Berrueta, D., Fernández, S., Frade, I.: Cooking http content negotiation with vapour. In: 4th Workshop on Scripting for the Semantic Web (SFSW), Citeseer (2008)Google Scholar

Web References

  1. 61.
    Mother IoT device: https://sen.se/store/mother/
  2. 62.
    Apple HealthKit: http://bit.ly/2xBFo8x
  3. 63.
    IoT Cisco’s predictions:http://bit.ly/2JqJLdj
  4. 64.
  5. 65.
  6. 66.
    Jena Framework Documentation: https://jena.apache.org/
  7. 67.
  8. 68.
  9. 69.
    Oops Web Service: http://oops-ws.oeg-upm.net/
  10. 70.
  11. 71.
  12. 72.
  13. 73.
    LOV Back End Java code on Github:https://github.com/pyvandenbussche/lovScripts
  14. 74.
    LOV JavaScript code for the GUI on GitHub:https://github.com/pyvandenbussche/lov
  15. 75.
  16. 76.
  17. 77.
  18. 78.
    LODE Java code on GitHub: https://github.com/essepuntato/LODE
  19. 79.
  20. 80.
    WebVOWL JavaScript code on GitHub: https://github.com/VisualDataWeb/WebVOWL
  21. 81.
  22. 82.
  23. 83.
    Vapour JavaScript API: http://vapour.sourceforge.net/api/
  24. 84.
  25. 85.
    NeON ontology methodology: http://neon-toolkit.org/
  26. 86.
  27. 87.
    Linked Data blog: http://linkeddata.org/home
  28. 88.
    Semantic Web Best Practices for Dummies Documentation: http://bit.ly/2XB9jsa
  29. 89.
    Slides step-by-step tutorial to improve the ontology quality, dissemination, reuse, etc. Semantic Web Best Practices: https://goo.gl/Rg4cGr
  30. 90.
    Domain Ontology Ranking System (DoORS) prototype: https://owlparser.herokuapp.com/
  31. 91.
    Ontology Design Patterns (ODPs) wiki: http://ontologydesignpatterns.org/

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Kno.e.sis, Wright State UniversityDaytonUSA
  2. 2.TrialogParisFrance
  3. 3.MondecaParisFrance
  4. 4.Insight Center for Data Analytics, National University of GalwayGalwayIreland

Personalised recommendations