Nutrition and agricultural innovation projects need an ontology vocabulary and food description system to optimally share their knowledge
Describe the People and Place: Provide information that would be helpful for an outsider who has never been there and may have no context about this Place to better understand the area.
Noting that we are not applying for this prize, I will say hopefully a visitor to Earth will be impressed by the geological, ecological, cultural and economic diversity of life. They will see however that the human anthroposphere is rapidly changing this interstellar tourist destination, and not in natures favour. They will be looking for signs that humans are not innately selfish creatures, driven by short term thinking, but that instead, in the face of dire circumstances, they can be generous, and creative, and able to apply new technology with foresight, or revise old wasteful habits in order to slowly restore symbiosis with the rest of life. It is a process of understanding ourselves - how much our lives have been connected to the agricultural revolution, and now, the digital revolution, and how that will affect the material and social sphere of our need. An outsider will see it isn't possible for everyone to be on the same page, but various projects around the globe will give hope for a better life and calmer world.
Challenges: Describe the current (2020) and the future (2050) challenges that your food system faces.
The many food system vision projects do an excellent job of summarizing the real, physical challenges our planet faces in terms of food security, nutrition and a changing environment. Our focus is on the digital landscape of data provided by terrestrial food related projects. Will the fruit of their knowledge development - the ideas generated by and for their communities - remain regionally siloed, or can we solve the reverse problem of needing to connect them, so that patterned reuse of their findings is possible?
How can researchers find agricultural datasets or food culture experiment results without having a common vocabulary to search with, or a federated platform of databases to search through? Google is indeed the home page for many of us looking for research questions and solutions others have explored, but whether now or in 2050 the onslaught of possibly relevant information is overwhelming. We need semantic technology to help us with the food language domain. Even the current data science 80/20 rule of thumb - spending 80% of one's time cleaning data, and 20% analyzing it, assumes one has found most relevant data.
Address the Challenges: Describe how your Vision will address the challenges described in the previous question.
Borrowing from the abstract of our 2018 inaugural FoodOn paper,
The construction of high capacity data sharing networks to support increasing government and commercial data exchange has highlighted a key roadblock: the content of existing Internet-connected information remains siloed due to a multiplicity of local languages and data dictionaries. This lack of a digital lingua franca is obvious in the domain of human food as materials travel from their wild or farm origin, through processing and distribution chains, to consumers. Well defined, hierarchical vocabulary, connected with logical relationships—in other words, an ontology—is urgently needed to help tackle data harmonization problems that span the domains of food security, safety, quality, production, distribution, and consumer health and convenience. FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world. FoodOn addresses food product terminology gaps and supports food traceability. Focusing on human and domesticated animal food description, FoodOn contains animal and plant food sources, food categories and products, and other facets like preservation processes, contact surfaces, and packaging. Much of FoodOn’s vocabulary comes from transforming LanguaL, a mature and popular food indexing thesaurus, into a World Wide Web Consortium (W3C) OWL Web Ontology Language-formatted vocabulary that provides system interoperability, quality control, and software-driven intelligence. FoodOn compliments other technologies facilitating food traceability, which is becoming critical in this age of increasing globalization of food networks.
FoodOn is evolving with better curation and domain-specific food term requests, and it plays just one part of the overall ecological, agricultural and cultural landscape of food related activity. We want to emphasize that there are many pertinent ontologies that should help standardize and enhance communication about vision project metadata, as indicated in the first diagram above.
High Level Vision: With these challenges addressed, now provide a high level description of how the Place and the lives of its People will be different than they are now.
It is hard to predict which vision projects will succeed or fail, but what should be available is a semantically organized matrix or set of dimensions by which to compare the similarity of projects, such that we can perform much better analysis of the factors that led to success or failure from an environmental, cultural, technical, or economic perspective. An ontology-driven data description framework in characterizing vision projects will also apply to similar academic, government and commercial projects.
Full Vision: How do you describe your Vision for a regenerative and nourishing food future for your Place and People for 2050?
Describing project datasets and metadata is part of a three-pronged ontology effort to satisfy FAIR data principles.
Interconnection between food source organisms and the products that are ultimately derived from them.
Overall set of relationships for describing a food product
Stating one last time that we're not applying for the prize - we just want to make sure projects can see the food language problem and a future on the horizon where ubiquitous data sharing is seen as the second equally valuable part of a project, whether deemed a failure or a success.
We are all increasingly connected to a digital food agriculture and distribution infrastructure. This shows in Internet of Things agricultural and shipping sensors, automated crop management, food distribution, consumer-sensitive marketing of food related information, and food-borne disease outbreak trace forward and trace backward activities. The cost of tagging food with digital information through its lifecycle is dropping, and so that volume of information is increasing, but there is a tendency for this kind of information to be proprietary - contained only within particular corporate distribution chains and barcode databases. Past efforts at what would now be characterized as an open source paradigm for creating a global food vocabulary seemed too daunting due to local language barriers, semantic variations of vocabulary, and slow data communication, but modern internet technology and semantic web concepts have resolved most of the concerns raised decades ago. This has led to the revisiting of global food vocabulary visions. Wikipedia has led the way in enabling open-source and multilingual and regionally sensitive description of food. Ontologies are adding to this publicly curated knowledge a more formal and so far mainly research community curated layer that computers and datasets can reference directly, thus enabling the data to excel at FAIR principles (https://www.go-fair.org/fair-principles/).
The biomedical research community has spearheaded a lot of development of controlled vocabularies – and especially OWL ontology formatted ontologies in an open source, collaboratively curated way for the past 15+ years, and the wealth of this work is showing in places like OBOFoundry.org, that lists mostly compatible and non-redundant ontologies. A number of these vocabularies are emerging as defacto standards for providing unambiguous references to cellular anatomy, experimental device and procedure terminology, among many others. All of these ontologies are searchable from https://www.ebi.ac.uk/ols/index, for those projects that want to get an idea of coverage for their data related terms.
Many research databases are turning to ontologies to finally standardize dataset parameters. For example, the FoodOn team is helping USDA agricultural research service categorize their food product nutrition information and cross reference it to CHEBI the Chemicals of Biological Interest ontology. We recognize that use of ontology in annotating project data and metadata is only one leg of three components of a successful semantic ecosystem. In addition to dataset curation, a feedback loop needs to connect to the ontology curation teams that are providing computationally accessible terms, ids and logic. As well the federated database tools that allow searching for relevant datasets need to reference or import project datasets that have been curated with ontology terms.
Included are other images which present the relationships involved in some of the food product related ontology work, and the connection that will ultimately exist between food products, their growing environments and agricultural treatments, nutritional test results, and more complex food composition, dietary intake, etc. Nailing down standardized language for each of these domains is crucial for coping with and gleaning insight from the data science universe.