An ontology for maintenance procedure

documentation to support Industry 4.0 software

applications

TLP Community of Interest Workshop,

2021 Model-Based Enterprise Summit,

NIST, April 12 - 16, 2021

Presented By:

Caitlin Woods (BSc, MPE, PhD Student)

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EXAMPLE MAINTENANCE

PROCEDURE DOCUMENT FROM AN

AUSTRALIAN PROCESS PLANT
Documents are diverse in content (i.e.

different engineers write procedures in

different ways)

Documents are diverse in structure (i.e. in

PDF templates that change over time)

Background

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Current Problems
Whatis an ontology?

'''a formal, explicit specification of a shared conceptualisation of concepts

within a domain." [1]

In essence, data is assigned classes (or concepts) and place (formal and

explicit) restrictions on the relationships between those classes.

Note that the relationship "denotes" comes from the shared information

artefact ontology and has its own definition. [2]

The use of these definitions enables reasoning.

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Machine

Machine

Identifier

denotes
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Module 1: The static procedure ontology
Module 2: The corrective maintenance task

ontology

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Special Feature 1: The generic task hierarchy

Ontology component

Example data
Special Feature 2: Corrective actions and limits

as failure modes

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Image taken from [3]
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The role of ontologies in TLP

SHARED VOCABULARY

Ontologies promote "shared" names for concepts

in a domain. Essential for TLP tasks where a

tagging schema is required such as in Named

Entity Recognition.

CONCEPTINTERPERETATION

The formal nature of ontologies ensure that

different parties (i.e. humans and machines in

TLP) interpret the meaning of concepts in the

same way.

FACTINFERENCE

Reasoning services enable ontology

users to infer new facts from data. For

example, in a particular context, a "tool"

can become a "resource".

DATAINTEGRATION

Ontologies enable us to integrate

concepts that may appear disparate.

This was the case with Functional

Failures in the procedure ontology.

This means the TLP pipelines could

draw from many data sources to

make decisions.

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The role of TLP in ontologies

DATAPOPULATION

TLP algorithms that can "tag" large sets of data according to a concept schema can be used to

populate an ontology. For example, a TLP algorithm may be able to recognise a "tool" from task

text. This tool can be stored as a "resource" in the ontology.

CONVERSATIONALINTERFACES

Ontologies and TLP can be used together to create natural language question answering

systems (i.e. a chatbot for maintenance).

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ONTOLOGYEXPLAINED BLOG

https://ontology-explained.com/

THEINDUSTRIAL

ONTOLOGIESFOUNDRY

https://www.industrialontologies.org/

MYPHD RESEARCH

https://caitlin2694.github.io/

Resources

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References

[3] Hodkiewicz, M., Klüwer, J. W., Woods, C., Smoker, T., & French, T. (2020). Digitalization

and reasoning over engineering textual data stored in spreadsheet tables. IFACPapersOnLine, 53(3), 239-244.

THIS PRESENTATION'S TEMPLATE, IMAGES AND ICONS ARE FROM THE CANVA ONLINE

DESIGN PLATFORM (https://www.canva.com/).

[1] R. Studer, V. R. Benjamins, and D. Fensel,“Knowledge engineering: principles and

methods,”Data & knowledge engineering, vol. 25, no. 1-2, pp. 161–197, 1998.

[2] OBO Foundry. (2021). Information Artifact Ontology

:An ontology of information entities. http://www.obofoundry.org/ontology/iao.html