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Semantic IoT: Theory and Applications pp 161–192Cite as

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

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Part of the book series: Studies in Computational Intelligence ((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.

Thanks to the Linked Open Vocabularies (LOV) team for sharing their expertise regarding the usage of validation tools.

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Notes

  1. 1.

    http://fiesta-iot.eu/.

  2. 2.

    “Thanks to Amelie Gyrard for the help on the project”.

  3. 3.

    https://ec.europa.eu/digital-single-market/en/alliance-internet-things-innovation-aioti.

  4. 4.

    http://www.neon-project.org/.

  5. 5.

    http://perfectsemanticweb.appspot.com/?p=ontology_sota.

  6. 6.

    http://perfectsemanticweb.appspot.com/perfecto/statusTool/?url={url}.

  7. 7.

    http://purl.org/perfecto.

  8. 8.

    https://www.cs.ox.ac.uk/isg/projects/LogMap/.

  9. 9.

    http://perfectsemanticweb.appspot.com/?p=ontology_sota#div_ontology_documentation_mindmap.

  10. 10.

    http://perfectsemanticweb.appspot.com/?p=ontologyValidationLOV4IoT.

  11. 11.

    http://purl.org/lov4iot-dataset.

  12. 12.

    http://perfectsemanticweb.appspot.com/?p=ontologyValidation.

  13. 13.

    http://perfectsemanticweb.appspot.com/?p=evaluation_lov4iot_perfecto.

  14. 14.

    http://perfectsemanticweb.appspot.com/?p=ontology_sota.

  15. 15.

    http://perfectsemanticweb.appspot.com/?p=updateCatalogueForm.

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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.

Author information

Authors and Affiliations

  1. Kno.e.sis, Wright State University, Dayton, USA

    Amélie Gyrard

  2. Trialog, Paris, France

    Amélie Gyrard

  3. Mondeca, 35, Boulevard Strasbourg, Paris, 75010, France

    Ghislain Atemezing

  4. Insight Center for Data Analytics, National University of Galway, Galway, Ireland

    Martin Serrano

Authors
  1. Amélie Gyrard
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  2. Ghislain Atemezing
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  3. Martin Serrano
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Corresponding author

Correspondence to Amélie Gyrard .

Editor information

Editors and Affiliations

  1. Amity Institute of Information Technology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India

    Rajiv Pandey

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  3. Amity Institute of Information Technology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India

    Nidhi Srivastava

  4. Database System Laboratory, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Subhash Bhalla

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Katarzyna Wasielewska-Michniewska

7 Appendix

7 Appendix

1.1 7.1 Catalogs of Tools

The PerfectO web site provides the Catalogs of tools menu (depicted in Fig. 8) to easily give access to software URLs and publications. The Catalogs of tools references tools for (1) ontology improvement which is the focus of this paper, (2) dataset quality, (3) querying, and (4) reasoning. Figure 9 demonstrates an extensive literature survey within the table of contents classified in two different ways:

  • A bullet list referencing tool’s URLs and scientific publications are displayed simply with clickable links (see Fig. 6 as an example).

  • Mind maps [55] are recognized as a useful methodology and a powerful graphics technique used to translate what’s in the mind into a visual picture. Since mind mapping works like the brain, it allows us to organize and understand information faster and better. We designed mind maps to answer Frequently Asked Questions (FAQs). Figure 11 shows the Mind maps for ontology catalogs. Figure 12 illustrates the Mind maps for ontology methodologies. Figure 13 illustrates the Mind maps for ontology validation.

The catalogs of tools are available onlineFootnote 14; it covers numerous research domains: (1) ontology documentation, (2) ontology catalogs and semantic search engines, (3) ontology methodologies, (4) ontology validators, (5) ontology validators for IoT, (6) ontology visualization, (7) collaborative vocabulary development, (8) ontology evaluation, and (9) ontology repair. Extensive work has been done in ontology documentation (not covered in the Related Work Section) and structured within a mind map, but referenced by the Catalogs of Tools as displayed in Fig. 10. We guide ontology designers with LODE and Parrot tool since web services are provided, as shown in Sect. 7.3 and Table 3. We cover some of those topics (ontology methodology, ontology evaluation, ontology metrics, quality, and relevant tools) in Sect. 3. Ontology designers can contribute to enrich the set of Catalogue of Tools by using a Google Form interface.Footnote 15

Table 3 Reusable tools for the ontology improvement
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Fig. 8
figure 8

Catalogs of tools

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Fig. 9
figure 9

State of the art classifying ontology improvement tools

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1.2 7.2 Dr. PerfectO Availability of Tools (DPAT)

DPAT tool (depicted in Fig. 7 and introduced in Sect. 4.1), online http://perfectsemanticweb.appspot.com/?p=availability_tools, checks the availability of reusable tools in case the server is down. Each row provides: (1) the name of the tool, (2) its usage, (3) the clickable tool’s URL, and (4) the tool availability (displayed as images: OK or NOT OKAY). For instance, the LODE ontology documentation web service runs well when DPAT has been deployed.

Fig. 10
figure 10

Mind map classifying ontology documentation tools

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Fig. 11
figure 11

Mind map classifying ontology catalog tools

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Fig. 12
figure 12

Mind map classifying ontology methodologies

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Fig. 13
figure 13

Mind map classifying ontology validation tools

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Table 4 Evaluation: IoT ontologies with tools for ontologies
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1.3 7.3 PerfectO Guidance: The Most Accessible Tools for Ontology Engineering

The learning curve for software engineering can be extremely high when developers are not familiar with the same programming languages and libraries used to build the tools. A set of tools that can be considered<easy-to-use> if: (1) The tools provide GUIs and web services, (2) the documentation is available and well-explained, (3) the ontologies can be evaluated with tools offering diverse functionalities, and (4) software setup configuration is not required.

The classification of tools that we have selected is available within Table 3 to support the Ontology Improvement methodology (explained in Sect. 4.2). The table is a way to organize the multiple tested technologies and if there is an available source for documenting it. For instance, WebVOWL tool can be used to provide automatic ontology graph visualization, Parrot for automatic documentation, etc. In the table, the first column is dedicated to the tool name, and scientific publication is available. The second column explains the requirement satisfied. The third column provides the GUI interface URL. The fourth column indicates the web service or API if available. The fifth column contains the code URL if accessible. The sixth column explains the maintainability of the tools. The web services are more convenient to integrate when developing the methodology, but the implementation depends on web reliability and the maintenance of web services. Sometimes the servers hosting the web services are down, or when new versions are released, it has an impact on the implementation. When the tools are open source, such dependencies are avoided, but it is more time-consuming for developers to get into the code based on various languages and technologies. It is another reason demonstrating the needs to help ontology designers. In Table 3, within the maintained column: High means that the community behind the tools is reactive when issues arise such as server down, fixing bugs, answering questions or adding new functionalities. Medium means that the tools is frequently down, due to server issues.

More tools will be integrated later since we are facing the issues of the availability of tools as well. For this reason, a parallel work was to develop the Dr. PerfectO Availability of Tools (DPAT) component demonstrator (introduced in Section 7.2).

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Gyrard, A., Atemezing, G., Serrano, M. (2021). PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies. In: Pandey, R., Paprzycki, M., Srivastava, N., Bhalla, S., Wasielewska-Michniewska, K. (eds) Semantic IoT: Theory and Applications. Studies in Computational Intelligence, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-64619-6_7

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