810 likes | 958 Views
Towards a Reference Terminology for Talking about Ontologies and Related Artifacts. Barry Smith http://ontology.buffalo.edu/smith with thanks to Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober. Problem of ensuring sensible cooperation in a massively interdisciplinary community. concept
E N D
Towards a Reference Terminology for Talking about Ontologies and Related Artifacts Barry Smith http://ontology.buffalo.edu/smith with thanks to Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober
Problem of ensuring sensible cooperation in a massively interdisciplinary community concept type instance model representation data
What do these mean? ‘conceptual data model’ ‘semantic knowledge model’ ‘reference information model’ ‘an ontology is a specification of a conceptualization’
natural language labels to make the data cognitively accessible to human beings and algorithmically tractable
compare: legends for maps compare: legends for maps
legends help human beings use and understand complex representations of reality help human beings create useful complex representations of reality help computers process complex representations of reality
computationally tractable legends help human beings find things in very large complex representations of reality
legends for mathematical equations xi = vector of measurements of gene i k = the state of the gene ( as “on” or “off”) θi = set of parameters of the Gaussian model ... ...
Glue-ability / integration rests on the existence of a common benchmark called ‘reality’ the ontologies we want to glue together are representations of what exists in the world not of what exists in the heads of different groups of people
a network diagram can be a veridical representation of reality
maps may be correct by reflecting topology, rather than geometry
an image can be a veridical representation of reality a labeled image can be a more useful veridical representation of reality
an image labelled with computationally tractable labels can be an even more useful veridical representation of reality
annotations using common ontologies can yield integration of image data
if you’re going to semantically annotate piles of data, better work out how to do it right from the start
First basic distinction type vs. instance (science text vs. diary) (human being vs. Tom Cruise)
For ontologies it is generalizations that are important = ontologies are about types, kinds
An ontology is a representation of types We learn about types in reality from looking at the results of scientific experiments in the form of scientific theories experiments relate to what is particular science describes what is general
There are created types bicycle steering wheel aspirin Ford Pinto we learn about these by looking at manufacturers’ catalogues
Inventory vs. CatalogTwo kinds of representational artifact Roughly: Databases represent instances Ontologies represent types
object organism animal cat siamese types mammal frog instances
if you’re going to semantically annotate piles of data, better work out how to do it right from the start
Entity =def anything which exists, including things and processes, functions and qualities, beliefs and actions, documents and software (Levels 1, 2 and 3)
First basic distinction universal vs. instance (science text vs. diary) (human being vs. Tom Cruise)
Ontology = A representation of universals • Each node of an ontology consists of: • preferred term (aka term) • term identifier (TUI, aka CUI) • synonyms • definition, glosses, comments
An ontology is a representation of universals We learn about universals in reality from looking at the results of scientific experiments in the form of scientific theories experiments relate to what is particular science describes what is general