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Why Computer Science Needs Philosophy

We begin with the question 'What is a science?", a question which was answered by the great 19th century Bohemian logician-philosopher-mathematician Bernard Bolzano as follows: A science is an idealized scientific textbook. A variant of this idea was propounded in the 20th century by the logical positivists of the Vienna Circle, for whom a science is a logically formalized theory; more specifically it is an (again idealized) set of propositions expressed in first order logic (FOL). The logical positivists conceived on this basis the idea of "unified science", which would be achieved by formalizing all scientific theories within a single framework. This view of science formed one pillar of the artificial intelligence work in Stanford in the 1970s and '80s, most conspicuously on the part of Patrick Hayes, who applied the idea to common science (or what he called "naive physics"). To achieve artificial intelligence would be to replicate the common sense beliefs of ordinary human beings in sets of propositions formalized using FOL, and use the results to drive (for example) a robot. Both the logical positivist idea of formalizing science and Hayes' idea of formalizing common sense beliefs failed in realizing their immediate goals. But they each formed important parts of the process which led to the establishment of ontology building as an important strategy in what we now call 'data science'. The purpose of this talk is to show that ontology building is more successful, and more useful, if the ontologies developed are based on a coherent philosophical idea of what an ontology is and what kinds of entities in the world an ontology represents. I illustrate the important of this idea by describing the problems that can arise for ontology builders and users in the absence of such a foundation. First, I take the example of the HL7 healthcare messaging standard and its Reference Information Model (RIM), which is based on the philosophically incoherent idea that 'every action is identical with its own documentation'. Second, I take the "Ontology 101", an influential guide to ontology building by two leaders in the field, Deborah McGuinness and Natasha Noy. The guide is based on the assumption that ontologies are about classes and that classes have instances. But its authors take the view that what is a class and what is an instance are questions whose answers depend on the use to which an ontology is put. Thus, they suggest, a kind of wine might be a class in ontology and an instance in a second ontology. This view, too, is philosophically incoherent, and leads to conclusions for instance to the effect that the Beaujolais region is a kind of France.

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