# Food Process Ontology Requirements

Damon Dooley<fnref n="1" />, Magalie Weber<fnref n="2" />, Liliana Ibanescu<fnref n="3" />, Matthew Lange<fnref n="4" />, Lauren Chan<fnref n="5" />,
Larisa Soldatova<fnref n="6" />, Hande K McGinty<fnref n="7" />, Chen Yang<fnref n="8" />, William Hsiao<fnref n="1" />

## Abstract

People often value the sensual, celebratory, and health aspects of food. However, behind this
experience exists many other value-laden agricultural production, distribution, manufacturing, and
physiological processes that support or undermine a sustainable future and a healthy population. The
complexity of such processes is evident in both every-day food preparation of recipes and in industrial
food manufacture, packaging and storage, each of which depends critically on human or machine
agents, chemical or organismal ingredient references, and the explicit instructions and implicit
procedures held in formulations or recipes. An integrated ontology landscape does not yet exist to
cover all the entities at work in this farm to fork journey.

It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains and their relationship, material, and more abstract organization and role entities. The challenge is how to make this merger appear to be, by analogy, one language, rather than nouns and verbs from a dozen or more dialects which can't be used directly in a sentence about some aspect of the farm to fork journey without translation or substantial dialect education. Some compromise may be necessary since a number of existing ontologies have adoption momentum. This work focuses on the ontology components - classes, object and data properties and annotations - needed for food process modeling or more general process modelling within the context of Open Biological and Biomedical Ontology Foundry (OBO) (Smith et al. 2007) or congruent ontologies. Ideally these components can be brought together in a general process ontology that can be specialized not only for the food domain but for carrying out other protocols as well, since many operations involved in food identification, preparation, distribution and storage - shaking, boiling, mixing, freezing, labeling, shipping etc. - are actually common to other activities including manufacturing and laboratory work. Note that class hierarchies of agricultural, culinary, etc. food processes themselves are evolving in various projects and are not dealt with here.

Crafting a generalized process ontology requires a wide community of domain-specific users to
critique it, as well as due diligence - an examination of the objectives, capabilities, and lessons learned
from existing process and protocol related ontology projects. We compare the Exact2 (EXperimental
ACTions) ontology (Soldatova et al. 2014)), PO<fnref n="2" /> (Pénicaud et al. 2019), and the OBI Ontology for
Biomedical Investigation (Bandrowski et al. 2016) planned process model, which have various
degrees of OBO compatibility, as well as PROV-O (Missier, Belhajjame, and Cheney 2013)

<footnote n="1">¹ Centre for Infectious Disease Genomics and One Health, Faculty of Health Science, Simon Fraser University, Burnaby, BC, Canada</footnote>

<footnote n="2">² INRAE, UR BIA, Biopolymères Interactions Assemblages, Nantes, France</footnote>

<footnote n="3">³ MIA-Paris - Mathématiques et Informatique Appliquées, Paris, France</footnote>

<footnote n="4">⁴ International Center for Food Ontology Operability Data and Semantics (IC-FOODS), Davis, California,
USA</footnote>

<footnote n="5">⁵ College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA</footnote>

<footnote n="6">⁶ Department of Computer Science, Brunel University, London, UK</footnote>

<footnote n="7">⁷ Department of Chemistry and Biochemistry, Ohio University, Athens, OH, USA</footnote>

<footnote n="8">⁸ Department of Food Technology, Safety and Health, Ghent University, Belgium</footnote>
(https://www.w3.org/TR/prov-o/), the Time Ontology (Little and Cox 2020) (https://www.w3.org/TR/owl-time/), and The Sensor, Observation, Sample, and Actuator (SOSA) ontology (Janowicz et al. 2019) which have significant adoption momentum.

There are a variety of capabilities and paradigm choices to discuss in searching for a generalized
process ontology. Ideally we can model both planned processes, which are steered by some protocol
and/or set of objectives, and unplanned processes that we find in the physical world, and which
themselves might be harnessed by planned processes. Process models may be able to model
autonomous behaviour of agents free to roam an environment, actively seeking or waiting for
contextual information which they are designed to react to; alternatively there are process models that
limit themselves to describing steps in a workflow, implicitly controlled by a more abstract layer.
Some process models can express characteristics of input and output entities, thus illustrating changes
in states of a system, or conditions that must be met for processing to continue; others code how a
process changes its input material right onto the process; some provide a clear way to specialize a
process into interdependent components; or conversely can provide abstracted views by hiding
underlying classes. Some rely upon the OWL-Time ontology to express dependencies between
process intervals; others have dependency relations expressed between the processes themselves, and
leave it to software to calculate Gantt chart interval dependencies and time points. Some process
ontologies create many object properties, others reduce object properties towards a generic set, and
rely on querying that specifies more conditions on the subject or object entities of a triple.

A remaining question is to what extent a generalized process ontology can use OBO ontologies exclusively, or should involve other ontologies like OWL-Time. More pragmatically, even if a mostly OBO solution was on the horizon, ontology reuse projects may not be able to wait for the necessary components for their domain to be created within OBO, and instead turn to other ontologies that have adoption momentum. A gap analysis about process modelling capability at least provides the grounding to make such decisions.

Keywords

Ontology, process ontology, OBO Foundry, food processing, process modelling

Acknowledgements

This work is primarily supported by the USDA Non-Assistance Cooperative Agreement
58-8040-8-014-F and Genome Canada Grant 286GET to W. Hsiao.

References

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