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Presentation at the Micro-Array Department, University of Amsterdam
data type, labelling, aggregation, generalisation
naming, scaling and units, confounding
domain, integrity constraints
Based on Goh (1996)
e.g. DB1has attribute name colour and value green and DB2 with color and 2DE60E
Data is different, but the conceptualisation is the same. Capture this agreement in an ontology.
Shorthand: “specification of a shared conceptualisation” (Gruber), but better: “An ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualisation of the world. The intended models of a logical language using such a vocabulary are constrained by its ontological commitment. An ontology indirectly reflects this commitment (and the underlying conceptualisation) by approximating these intended models.” (Guarino, 1998).
Catalogue of normalised terms: is a simple list without inclusion order, axioms or glosses.
Glossed catalogue: a catalogue with natural language glossary entries, e.g. a dictionary of medicine.
Prototype-based ontology: types and subtype are distinguished by prototypes rather than definitions and axioms in a formal language
Taxonomy: is a collection of concepts having a partial order induced by inclusion.
Axiomatised taxonomy: as taxonomy, but then with axioms and stated in a formal language.
Context library / axiomatised ontology: a set of axiomatised taxonomies with relations among them, like the inclusion of one context into another one, or the use of a concept from one in the other one.
athletic game(x) game(x)
court game(x) athletic game(x) y. played_in(x,y) court(y)
tennis(x) court game(x)
double fault(x) fault(x) y. part_of(x,y) tennis(y)
NT athletic game
NT court game
RT double fault
precision: the ability to catch all and only the intended meaning
(for a logical theory, to be satisfied by intended models)
Increase in (perceived) difficulty of operation
Unification, total compatibility, merging [similar subject domains]
Merging [different subject domains], partial compatibility
Mapping, approximations, helper model, alignment, intersection ontology
Extending, incremental loading
Use in/for applications
Queried ontologies, hybrid ontologies
Increase in level of integration
Using foundational aspects in ontology development decreases the chance of design inconsistencies and facilitates integration
E.g. the OBO phenotype ontology does not:
%attribute\:excretory_function ; PATO:00300204
%attribute\:urination ; PATO:00305204
%attribute\:urine_composition ; PATO:00301204
Allow multiple inheritance - or not?
kinds, formalisation, bias & bioscience
categorisation, some challenges
Formation of a theory
Formation of hypothesis
New empirical axioms/laws (universal)
Empirical axioms/laws (universal)
Facts with an empirical basis
- Facilitate knowledge reuse, interoperability
Another item in the problem-solver’s toolbox
Part of a new/improved software system
- SW tools for ontology development, maintenance, integration
- Top-level ontologies
Attempt to understand, what/why
- W.r.t. bioscience
Part of falsification paradigm and steps 2, 3 of standard view
-> synergy, mutually beneficial process, but…
-> ontology subject to (extensive) modification. Complicates integration
-> accommodate this in an ontology? E.g. a library of ‘alternative views ontologies’, with loose coupling instead of integration?
-> capture what is, what can be, (and what might be?)
-> more here
-> interdisciplinary work of ontologists with scientists
-> bottom-up resp. top-down procedures for ontology development
Are these aspects real challenges, or due to limited expressiveness of non-formal approaches and software modelling paradigms (ER, OO, …), or maybe due to limited knowledge of both the domain expert and ontologist?
No legacy, no full knowledge of UoD.
-> Former might be alleviated over time; double curriculum, but
still difference in ‘science’ and ‘engineering’ approaches
radations/non-discrete data, occasional relationships, conditionality.
-> Separate layer of sw, or semantics intricate part of bio data?
Uncertainties, ‘postulations’, importance of parameters, properties.
-> characteristic of conducting scientific research; lack vs
abundance of data can be argued as design decision, not
characteristic of the data/concepts; ‘upgrading’ of concepts
Definitional problems and lack of standardisation in nomenclature.
-> Is the surface layer of next point; overabundance of ‘semi-
standards’; can be in itself interdisciplinary within bioscience
isagreements between and within research groups, ‘alternative’
hypotheses and theories coexist.
-> There is not one ‘what is’; development of multiple theories,
concepts before agreement is part of doing scientific
research; library of models, aliasesMain biological data characteristics
Emphasis core sciences:
‘All-inclusive’ comprehensive models
Emphasis applied bioscience:
Conceptually representing the integration
of various core disciplines,
Only what is relevant in limited context
Formal Ontology www.formalontology.it
RE Kent www.ontologos.org
WonderWeb project http://wonderweb.semanticweb.org
JF Sowa www.jfsowa.com/ontology/index.htm
AAAI page http://www.aaai.org/AITopics/html/ontol.html
KAON http://km.aifb.uni-karlsruhe.de/kaon2, Protégé http://protege.stanford.edu, VU http://www.cs.vu.nl/, STARLab www.starlab.vub.ac.be/default.htm