W3C Life Science Ontology Issues Session on Triples and Ontologies

Why the Tower of Babel Exists Biomedical science is a bottom up enterprise  Efficiency of competitive systems  Multiple independent discovery External enabling technology and knowledge Tension between dissemination and control  Fundamental desire to be cited  Fundamental need to control intellectual property Implicit citation through nomenclature  If you are using my name, you are citing my discovery

Name Space Collisions Molecular biology has an extraordinarily complex vocabulary  Many terms with highly specific meanings that are used rarely  Plasmid, pUC13, M13, cosmid, fosmid, yac, bac, pac, … All cloning vectors, each with specific properties and uses  High information content per word Compression through acronyms => collisions across domains  PCR Polymerase Chain Reaction Historically, MeSH indexed PCR as an abbreviation for “premature contraction” in cardiology Phosphocreatine in metabolism and physiology Specific definitions with high information content  Association Generally a rather vague relationship In statistical genetics, a precisely defined criteria implying that specific tests for significance have been met.

Biomedical Text is Not “Well Classifiable” Classifiable domain  Well defined robust classes  Class definitions ~robust to algorithms and metrics Poorly classifiable domains  Class boundaries not clear, class definitions not robust  Really just saying the best classification is one big class Biomedical text is a web, not a collection of well defined domain specific corpuses  Is an article about P53 molecular biology, gene expression regulation or cancer biology?

Probabilistic Nature of Biomedical Knowledge Bayes rule  I know what I have observed  I can only probabilistically rank hypotheses  Understanding evolves as more data becomes available Language links to understanding  As the understanding evolves, the meaning of the language evolves  Ask 3 biologists to define a gene and you will get 5 definitions and 2 dissenting opinions

Questions for Ontologies Session How to represent probabilistic concepts and meanings with logically precise standards? How do we associate the appropriate domain specific ontology(ies) with the text we are analyzing? How do we create sustainable merges across evolving domain specific ontologies?