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Data and ontology integration issues in the biosciences Marijke Keet Napier University, 10 Colinton Road, Edinburgh EH10 5DT /

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Presentation on theme: "Data and ontology integration issues in the biosciences Marijke Keet Napier University, 10 Colinton Road, Edinburgh EH10 5DT /"— Presentation transcript:

1 Data and ontology integration issues in the biosciences Marijke Keet Napier University, 10 Colinton Road, Edinburgh EH10 5DT / Presentation at the Micro-Array Department, University of Amsterdam

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. DB 1 has attribute name colour and value green and DB 2 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) Heavyweight ontologies Lightweight ontologies 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. Formal ontology Informal ontology Semi-formal ontology?

9 Formalisations (2-2) Ontological precision Axiomatized theory Glossary Thesaurus Taxonomy DB/OO scheme tennis football game field game court game athletic game outdoor game Catalog game athletic game court game tennis outdoor game field game football game NT athletic game NT court game RT court NT tennis RT double fault 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) 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 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?levels

12 Ontology integration (2-4) back Example structure/semantics

13 Ontology integration (3-4) Use in/for applications Increase in level of integration Unification, total compatibility, merging [similar subject domains] Merging [different subject domains], partial compatibility Mapping, approximations, helper model, alignment, intersection ontology Queried ontologies, hybrid ontologies Extending, incremental loading Increase in (perceived) difficulty of operation Initial categorisation

14 Ontology integration (4-4) (In)formal ontologies (In)consistencies in ontology design decisions during development (relationships) detail 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 example Conflicting goals More conflicts and mismatches here here Some challenges

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 ; PATO: %attribute\:urination ; PATO: %attribute\:urine_composition ; PATO:

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 -> 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) New empirical axioms/laws (universal) (4) Facts with an empirical basis (1) Empirical axioms/laws (universal) (2) Formation of a theory Explanation Confirmation Formation of hypothesis Induction, confirmation Prediction Confirmation Prediction

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 -> 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. -> ontology subject to (extensive) modification. Complicates integration Concepts underspecified, hypotheses and theories exist simultaneously. -> 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?) Biological data is more complicated than technological and practice data. -> more here here Systems Thinking, integrative concepts, holism and process-orientation contradict with objectifying knowledge in ontologies -> interdisciplinary work of ontologists with scientists Empiricism and the theoretical methodology in life sciences. -> bottom-up resp. top-down procedures for ontology development

22 Formalising biological knowledge Challenging biological data characteristics detail 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 detail next back

23 Main biological data characteristics No legacy, no full knowledge of UoD. -> Former might be alleviated over time; double curriculum, but still difference in science and engineering approaches Gradations/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 Disagreements 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, aliases 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 January. 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. 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/information on ontologies LOA IFOMIS Ontology Formal Ontology RE Kent WonderWeb project JF Sowa SUMO AAAI page Links to a few of groups developing tools KAON Protégé VU STARLab

28 Thank you!


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