Ontologies for Terminologies, Knowledge Representation & Software: Benefits & Gaps (“Don’t make the tea”) (Only a part of Knowledge Representation) Alan.

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Presentation transcript:

Ontologies for Terminologies, Knowledge Representation & Software: Benefits & Gaps (“Don’t make the tea”) (Only a part of Knowledge Representation) Alan Rector

What I do… ►Medical Terminologies ►ICD-11 ►SNOMED Quality Asssurance ►GALEN ►Tools ►Protégé-OWL / CO-ODE ►OPPL: patterns & scripts ► OWL-Patch: diff/patch for OWL ►HOBO: ontology driven architectures ►Commercial clinical systems (Siemens Health) ►Alternative User Facing User Interfaces ►Ontology Driven Architectures for Clinical Systems ►OWL Power users ►And embedding of OWL in hybrid systems 2

3 Some benefits ►Composition “Burn has_site some (Foot that has_laterality some Left) & has_penetration some Full_thickness & has_extent …” ►Avoid combinatorial explosion – Smaller terminologies that say more Support for expressions as well as names (“post-coordination”) ►Express context The “size of elephants” vs the “size of mice” ►Coordinate hierarchies and index information “Cancer”,”Family history of cancer”, “Treatment of cancer”, “Risk of cancer”, “Data structure for cancer”, “Data entry form for cancer”, “Pointer to rules for Cancer”, … ►Explicitness ►Can say precisely what concepts mean Can generate text back to see if we have said what we meant ►Often cuts costs by shortening meetings ►Inferred poly-hierarchies / DAGs

Some limitations ►Standards do not support all the operations needed ►Much information is hard to extract, e.g. ‘ What do we know about Cancer?’ ►Mixed queries: Lexical, semantics, annotaton, inference ►Models of metadata and annotation ►Engineering tools limited (“ODEs” are not yet adequate) ►The life cycle from elicitation to testing to implementation to revision, version management… ►Imports can only add, not over-ride ►User facing “intermediate representations”, patterns, and transformations ►“Hardening” - how to make a brittle technology predictable ►Relation to Software Engineering ill defined ►Template-based formalisms (UML, Frames) ►Java object models ►informal representations – SKOS, linked data, RDF(S) ►Relation to other Knowledge Representation often misunderstood ►Need KR systems supporting defaults and exceptions, probability, “same-kind--as”, higher order reasoning, both closed and open world reasoning, calculations, … … … ►Knowledge is more than definitions ! 4