THE DROIDS YOU’RE LOOKING FOR
Integrating, or fusing, data between two domains—for example, logistics and medicine—is a challenge, not only because those domains have specialized vocabularies, but also because they have different concepts. For fusion, we need a structure that can recon- cile conceptual or semantic reasoning—both what something is, as well as how it relates to other things.
For the logistics community, a ventilator is a piece of equipment that is manufactured, stored, maintained and shipped. In the medical profession, a ventilator requires power, has a setting for oxygen level, is assigned to one patient at a time and may require certain medications for intubation. Knowing how best to get a machine from point A to point B is only part of the problem. A ventilator only represents a life saved if it arrives on time for a patient and the facility has the other elements required for its successful use.
Data standards, that is, how data elements are defined and format- ted, do not alone provide sufficient architecture for data fusion. For fusion, we need a structure that can reconcile conceptual or semantic reasoning in addition to allowing us to seamlessly send data between systems.
HEATING UP
Experience tells people that a gas burner and a beaker are perfect for heating a liquid, but computers need more context. An ontology will help provide that. (Image by Getty Images/Ian Logan)
IT’S ALL ABOUT RELATIONSHIPS Te term “machine-understandable” harkens back to Skynet and killer robots—after all, if machines develop understanding, they must get how easy it would be to eliminate humans and take over the planet. Machine understanding, however, is more of a mechanical understanding than an existential one. An AI application may pass a Turing test, demonstrating its ability to behave in a convincingly human manner, but it will still lack other human traits, like appreciation of art or humor. While a standards-based data exchange like the National Information Exchange Model (NIEM), which DOD nominally adopted in 2013, offers a framework for linking data elements across func- tional domains, it does not provide the structure necessary for a computer to move beyond representation of data and informa- tion to modeling knowledge or understanding. For that, we will need to adopt an ontology.
Te need for a broader AI tool was highlighted by the Army National Guard’s efforts to support the national COVID-19 response. Te question of how to ensure that supplies got to a defined point of need was complicated by the spread of the virus, which reprioritized needs as new clusters emerged. Before we could align the supply chain for personal protective equipment or ventilators, we needed to know which hospital would require those supplies based on the spread of the virus.
140 Army AL&T Magazine Summer 2020
An ontology is a semantic model of data—that is, meaning is an emergent feature of how the data are related. In other words, it is a framework for applying shared meaning to data that humans and computers can understand. Te building blocks of this model are “triples,” each containing a subject, a predicate and an object, which are understandable by both humans and comput- ers. Te subject and object are data elements, and the predicate describes the relationship between them. Tese relationships
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