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Building and Using Ontologies

Building and Using Ontologies. Robert Stevens Department of Computer Science University of Manchester Manchester UK. Introduction. The nature of bioinformatics resources What is knowledge? What is an ontology? What are the uses of ontologies? Components of an ontology

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Building and Using Ontologies

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  1. Building and Using Ontologies Robert Stevens Department of Computer Science University of Manchester Manchester UK http://img.cs.man.ac.uk/stevens

  2. Introduction • The nature of bioinformatics resources • What is knowledge? • What is an ontology? • What are the uses of ontologies? • Components of an ontology • Building an ontology (in brief) http://img.cs.man.ac.uk/stevens

  3. The Nature of Bioinformatics Resources • Over 500 databanks and analysis tools that work over resources • Repositories of knowledge and data and generation of new knowledge • Knowledge often held as free text; some use made of controlled vocabularies • Enormous amount of semantic heterogeneity and poor query facilities • Knowledge about services not always apparent http://img.cs.man.ac.uk/stevens

  4. PatriciaGraceKennedysaid mine is a pint …CEKENN… Single letter amino acid codes C – cysteine K - lysine namenounverb What is Knowledge? Pat Baker is a Manchester bioinformatician who drinks beer. Protein that acts as a tyrosine kinase in the liver of primates. • Knowledge – all information and an understanding to carry out tasks and to infer new information • Information -- data equipped with meaning • Data -- un-interpreted signals that reach our senses PATRICIAGRACEKENNEDY SAIDMINEISAPINT http://img.cs.man.ac.uk/stevens

  5. Capturing Knowledge • Capturing knowledge for both humans an computer applications • A set of vocabulary definitions that capture a community’s knowledge of a domain • `An ontology may take a variety of forms, but necessarily it will include a vocabulary of terms, and some specification of their meaning. This includes definitions and an indication of how concepts are inter-related which collectively impose a structure on the domain and constrain the possible interpretations of terms.' http://img.cs.man.ac.uk/stevens

  6. What Does an Ontology Do? • Captures knowledge • Creates a shared understanding – between humans and for computers • Makes knowledge machine processable • Makes meaning explicit – by definition and context http://img.cs.man.ac.uk/stevens

  7. What is an Ontology? Thesauri “narrower term” relation General Logical constraints Catalog/ ID Formal is-a Frames (properties) Terms/ glossary Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part-of… http://img.cs.man.ac.uk/stevens

  8. Roles of Ontologies in Bioinformatics • We can divide ontology use into three types: • Domain-oriented, which are either domain specific (e.g. E. coli) or domain generalisations (e.g. gene function or ribosomes); • Task-oriented, which are either task specific (e.g. annotation analysis) or task generalisations (e.g. problem solving); • Generic, which capture common high level concepts, such as Physical, Abstract and Substance. Important in ontology management and language applications. http://img.cs.man.ac.uk/stevens

  9. Uses of Ontology • Community reference -- neutral authoring. • Either defining database schema or defining a common vocabulary for database annotation -- ontology as specification. • Providing common access to information. Ontology-based search by forming queries over databases. • Understanding database annotation and technical literature. • Guiding and interpreting analyses and hypothesis generation http://img.cs.man.ac.uk/stevens

  10. Components of an Ontology • Concepts: Class of individuals – The concept Protein and the individual `human cytochrome C’ • Relationships between concepts • Is a kind of relationship forms a taxonomy • Other relationships give further structure – is a part of • Axioms – Disjointness, covering, equivalence,… http://img.cs.man.ac.uk/stevens

  11. Knowledge Representation • Ontology are best delivered in some computable representation • Variety of choices with different: • Expressiveness • The range of constructs that can be used to formally, flexibly, explicitly and accurately describe the ontology • Ease of use • Computational complexity • Is the language computable in real time? Rigour -- Satisfiability and consistency of the representation • Systematic enforcement mechanisms • Unambiguous, clear and well defined semantics http://img.cs.man.ac.uk/stevens

  12. Languages • Vocabularies using natural language • Hand crafted, flexible but difficult to evolve, maintain and keep consistent, with weak semantics • Gene Ontology • Object-based KR: frames • Extensively used, good structuring, intuitive. Semantics defined by OKBC standard • EcoCyc (uses Ocelot) and RiboWeb (uses Ontolingua) • Logic-based: Description Logics • Very expressive, model is a set of theories, well defined semantics • Automatic derived classification taxonomies • Concepts are defined and primitive http://img.cs.man.ac.uk/stevens

  13. Building Ontologies • No field of Ontological Engineering equivalent to Knowledge or Software Engineering; • No standard methodologies for building ontologies; • Such a methodology would include: • a set of stages that occur when building ontologies; • guidelines and principles to assist in the different stages; • an ontology life-cycle which indicates the relationships among stages. http://img.cs.man.ac.uk/stevens

  14. The Development Lifecycle • Two kinds of complementary methodologies emerged: • Stage-based, e.g. TOVE [Uschold96] • Iterative evolving prototypes, e.g. MethOntology [Gomez Perez94]. • Most have TWO stages: • Informal stage • ontology is sketched out using either natural language descriptions or some diagram technique • Formal stage • ontology is encoded in a formal knowledge representation language, that is machine computable • the informal representation helps the former • the formal representation helps the latter. http://img.cs.man.ac.uk/stevens

  15. A Provisional Methodology • A skeletal methodology and life-cycle for building ontologies; • Inspired by the software engineering V-process model; • The overall process moves through a life-cycle. The left side charts the processes in building an ontology The right side charts the guidelines, principles and evaluation used to ‘quality assure’ the ontology http://img.cs.man.ac.uk/stevens

  16. Ontology in Use The V-model Methodology Evaluation: coverage, verification, granularity Identify purpose and scope Knowledge acquisition User Model Conceptualisation Principles: commitment, conciseness, clarity, extensibility, coherency Conceptualisation Integrating existing ontologies Conceptualisation Model Encoding/Representation principles: encoding bias, consistency, house styles and standards, reasoning system exploitation Encoding Representation Implementation Model http://img.cs.man.ac.uk/stevens

  17. The ontology building life-cycle Identify purpose and scope Knowledge acquisition Building Language and representation Conceptualisation Integrating existing ontologies Available development tools Encoding Evaluation http://img.cs.man.ac.uk/stevens

  18. Starting Concept List • Chemicals – atom, ion, molecule, compound, element; • Molecular-compound, ionic-compound, ionic-molecular-compound, …; • Ionic-macromolecular-compound and ionic-small-macromolecular-compound; • Protein, peptide, polyprotein, enzyme, holoprotein, apoprotein,… • Nucleic acid – DNA, RNA, tRNA, mRna, snRNA, … http://img.cs.man.ac.uk/stevens

  19. Conceptualisation Sketch Chemical Molecule Compound Element Ion Atom Molecular Compound Ionic Compound Molecular Element Ionic Molecule Non-Metal Metal Ionic Molecular Compound Metaloid http://img.cs.man.ac.uk/stevens

  20. Molecule Conceptualisation Sketch Ionic Macromolecular Compound Macromolecule Small Molecule Polysaccharide Protein Nucleic Acid Peptide Starch Glycogen Enzyme DNA RNA snRNA mRNA tRNA rRNA http://img.cs.man.ac.uk/stevens

  21. Initial Encoding class-def chemical subclass-of substance class-def molecule subclass-of chemical class-def compound subclass-of chemical class-def molecular-compound subclass-of molecule and compound http://img.cs.man.ac.uk/stevens

  22. Molecules Revisited Non-Ionic Macromolecular Compound Ionic Macromolecular Compound Macromolecule Small Molecule Polysaccharide Protein Nucleic Acid Peptide Starch Glycogen Enzyme DNA RNA snRNA mRNA tRNA rRNA http://img.cs.man.ac.uk/stevens

  23. More Encoding class-def chemical subclass-of substance class-def defined molecule subclass-of chemical Slot-constraint contains-bond min-cardinality 1 has-value covalent-bond class-def defined compound subclass-of chemical Slot-constraint has-atom-types greater-than 1 class-def defined molecular-compound subclass-of molecule and compound http://img.cs.man.ac.uk/stevens

  24. Expansion • Sketch and encode in cycles • Build a taxonomy of a small portion • Then build links to other portions • Add more detail • Document sources, author, date and argumentation. http://img.cs.man.ac.uk/stevens

  25. Summary • An ontology captures knowledge for a shared understanding • The important question is not whether an artefact is an ontology, but whether it does any good • Making our understanding of domain explicit, consistent and processable • Bioinformatics resources are knowledge resources – needs to be both human and machine understandable http://img.cs.man.ac.uk/stevens

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