Data%20and%20ontology%20integration%20issues%20in%20the%20biosciences

1
Data and ontology integration issues in the
biosciences
Presentation at the Micro-Array Department,
University of Amsterdam 23-8-2004

• Marijke Keet
• Napier University, 10 Colinton Road, Edinburgh
  EH10 5DT m.keet_at_napier.ac.uk / marijke_at_meteck.org

2
Overview presentation

• Data integration ontology
• Ontologies
• kinds, formalisation, bias bioscience after
  the break
• Ontology integration
• categorisation, some challenges

3
Overview presentation

• Data integration ontology
• Ontologies
• kinds, formalisation, bias bioscience after
  the break
• Ontology integration
• categorisation, some challenges

4
Data heterogeneity

• Schematic
• data type, labelling, aggregation,
  generalisation
• Semantic
• naming, scaling and units, confounding
• Intensional
• domain, integrity constraints

Based on Goh (1996)
5
Integrating data
e.g. DB1 has 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).
6
Overview presentation

• Data integration ontology
• Ontologies
• kinds, formalisation, bias bioscience after
  the break
• Ontology integration
• categorisation, some challenges

7
Kinds of ontologies

• Representation ontologies conceptualisations
  that underlie knowledge representation
  formalisms.
• Top-level ontologies generic and intermediate
  ontology concepts. This can be on top of a domain
  ontology or as stand-alone effort main aspect is
  domain independence.
• Generic ontologies consist of the general,
  foundational aspects of a conceptualisation (a
  lower branch in a top-level)
• Intermediate ontologies are slightly more
  tailored towards a conceptualisation of a
  specific domain. There may not be references to
  generic ontologies.
• Domain ontologies specialize in a subset of
  generic ontologies in a domain or sub-domain.
• Application ontologies () the UoD is even
  narrower than a domain ontology.

8
Levels of formalisation (1-2)
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.
Lightweight ontologies
Informal ontology
Semi-formal ontology?
Heavyweight ontologies
Formal ontology
9
Formalisations (2-2)
game athletic game court game tennis
outdoor game field game football
game(x) ? activity(x) 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)
tennis football game field game court
game athletic game outdoor game
Axiomatized theory
Taxonomy
game NT athletic game NT court game RT
court NT tennis RT double fault
Glossary
DB/OO scheme
Catalog
Thesaurus
Ontological precision

precision the ability to catch all and only the
intended meaning (for a logical theory, to be
satisfied by intended models)
Gangemi (2004)
10
Overview presentation

• Data integration ontology
• Ontologies
• kinds, formalisation, bias bioscience after
  the break
• Ontology integration
• categorisation, some challenges

11
Ontology integration (1-4)

• Combining different conceptualisations (views on
  reality) somehow.
• System, language/syntax, structure, and semantic
  integration. Latter most difficult.
• Structure and/versus semantic integration example
  
• Anarchy of terminology, definitions and
  methodologies (now at least 24 terms and 48
  definitions methodologies)
• Organise into levels of integration. Develop
  taxonomy of ontology integration?

12
Ontology integration (2-4)
Example structure/semantics
back
13
Ontology integration (3-4)
Initial categorisation
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
14
Ontology integration (4-4)
Some challenges

• (In)formal ontologies
• (In)consistencies in ontology design decisions
  during development (relationships) detail
• Top-down versus/combined with bottom-up
• Using foundational aspects in ontology
  development decreases the chance of design
  inconsistencies and facilitates integration
• Subject domain heterogeneity example
• Conflicting goals
• More conflicts and mismatches here

15
(In)consistencies in ontology design decisions
(1-2)

• Subsumption versus instantiation if A isA B,
  then all instances of A are also instances of B.
  The latter says a instanceOf A, i.e. a is an
  individual (particular, instance) and not a
  subtype of A.
• Desiderata to create the hierarchy. Like keeping
  function, structure, process separate.
• E.g. the OBO phenotype ontology does not
• attribute\excretory_function PATO00300204
• attribute\urination PATO00305204
• attribute\urine_composition PATO00301204

16
(In)consistencies in ontology design decisions
(2-2)

• E.g. aseptate hypha isa hypha aseptate hypha
  without cross walls and hypha in mycelium isa
  hypha. Former is about a special kind of hypha,
  the latter takes topology as distinction for
  subtyping -gt are distinct factors though treated
  as a same kind of isA where in the FAO hypha
  subsumes both.
• Allow multiple inheritance - or not?
• partOf such as parthood, proper parthood,
  connection, external connection, tangential
  parthood, interior parthood, partial coincidence
  and located-in (see e.g. Smith and Rosse, 2004
  Donnelly, 2004)
• Properties and meta properties (see Guarino and
  Welty (2000) for details)

back
17
Conflicts and mismatches

• Factors affecting ontology
  combination tasks
• Practical problems finding matchings, diagnosis
  repeatability, software usability, social
  factors of cooperation, goals
• Mismatches between ontologies
• - language level
• syntax, logical representation, semantics of
  primitives, language expressivity, precision
• - ontology level
• - conceptualisation
• content/UoD, concept scope, relationship
  scope, context, aggregation, accuracy
• - explication
• terminological hyper-/hyponyms
  (generalization), homonyms, synonyms
• modelling style paradigm, entity/concept
  description
• encoding
• Versioning identification, traceability,
  translation

18
Overview presentation

• Data integration ontology
• Ontologies
• kinds, formalisation, bias bioscience
• Ontology integration
• categorisation, some challenges

19
Ontologies for bioscience (1-3)
Theory (3)
Formation of a theory
Formation of hypothesis
Explanation
Confirmation
New empirical axioms/laws (universal) (4)
Empirical axioms/laws (universal) (2)
Confirmation
Prediction
Induction, confirmation
Prediction
Facts with an empirical basis (1)
20
Ontologies for bioscience (2-3)

• Ontologies as engineering artefacts
• - Facilitate knowledge reuse, interoperability
• Modelling practice
• Another item in the problem-solvers toolbox
• Part of a new/improved software system
• - SW tools for ontology development,
  maintenance, integration
• Ontologies embedded in science
• - Top-level ontologies
• Attempt to understand, what/why
• - W.r.t. bioscience
• Co-defining concepts?
• Part of falsification paradigm and steps 2, 3
  of standard view
• -gt synergy, mutually beneficial process, but

21
Ontologies for bioscience (3-3)

• The very essence of scientific progress is
  change, redefinition and creation of new
  concepts.
• -gt ontology subject to (extensive)
  modification. Complicates integration
• Concepts underspecified, hypotheses and theories
  exist simultaneously.
• -gt accommodate this in an ontology? E.g. a
  library of alternative views ontologies, with
  loose coupling instead of integration?
• -gt capture what is, what can be, (and what
  might be?)
• Biological data is more complicated than
  technological and practice data.
• -gt more here
• Systems Thinking, integrative concepts, holism
  and process-orientation contradict with
  objectifying knowledge in ontologies
• -gt interdisciplinary work of ontologists
  with scientists
• Empiricism and the theoretical methodology in
  life sciences.
• -gt bottom-up resp. top-down procedures for
  ontology development

22
Formalising biological knowledge

• Challenging biological data characteristics
  detail
• 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?
• Applied sciences within bio (medicine, ecology,
  environmental sciences), contexts detail

back
next
23
Main biological data characteristics
back
24
Applied bioscience
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 example
back
25
Example applied bioscience
back
26
References and more info (1-2)

• Donnelly, M. (2004). On parts and holes the
  spatial structure of the human body. MEDINFO
  2004, San Francisco, USA.
• Gangemi, A. (2004). Some design patterns for
  domain ontology building and analysis. Manchester
  15-16 January. www.loa-cnr.it/Tutorials.html
• Goh, C.H. (1996). Representing and reasoning
  about semantic conflicts in heterogeneous
  information sources. PhD, MIT.
• Guarino, N. (1998). Formal Ontology and
  Information Systems. In Formal Ontology in
  Information Systems, Proceedings of FIOS'98,
  Trento, Italy, Amsterdam IOS Press.
• Guarino, N. and Welty, C. (2000). A formal
  ontology of properties. Proceedings of 12th Int.
  Conf. on Knowledge Engineering and Knowledge
  Management, Lecture Notes on Computer Science,
  Springer Verlag.
• Keet, C.M. (2004). Ontology development and
  integration for the biosciences. Technical
  Report, Napier University, Edinburgh, UK.
  www.meteck.org/research.html
• Smith, B. and Rosse, C. (2004). The role of
  foundational relations in the alignment of
  biomedical ontologies. Proceedings of MEDINFO,
  San Francisco, USA.

27
References and more info (2-2)

• Some websites with different perspectives/aims/inf
  ormation on ontologies
• LOA www.loa-cnr.it
• IFOMIS www.ifomis.de
• Ontology www.ontology.org
• Formal Ontology www.formalontology.it
• RE Kent www.ontologos.org
• WonderWeb project http//wonderweb.semanticweb.or
  g
• JF Sowa www.jfsowa.com/ontology/index.htm
• SUMO http//ontology.teknowledge.com/
• AAAI page http//www.aaai.org/AITopics/html/ontol
  .html
• Links to a few of groups developing tools
• KAON http//km.aifb.uni-karlsruhe.de/kaon2,
  Protégé http//protege.stanford.edu, VU
  http//www.cs.vu.nl/, STARLab www.starlab.vub.ac.b
  e/default.htm

28
Thank you!