1 Building and Using Ontologies Robert Stevens Department of Computer Science University of Manchester Manchester UK

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)

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

4 What is Knowledge? 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 Patricia Grace Kennedy said mine is a pint name noun verb Pat Baker is a Manchester bioinformatician who drinks beer. …CEKENN… Single letter amino acid codes C – cysteine K - lysine Protein that acts as a tyrosine kinase in the liver of primates.

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.'

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

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

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.

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

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,…

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

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

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.

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: 1.Informal stage ontology is sketched out using either natural language descriptions or some diagram technique 2.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.

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

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

17 The ontology building life- cycle Identify purpose and scope Knowledge acquisition Evaluation Language and representation Available development tools Conceptualisation Integrating existing ontologies Encoding Building

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, …

19 Conceptualisation Sketch Chemical AtomElementCompoundMoleculeIon MetalNon-Metal Metaloid Molecular Compound Molecular Element Ionic Compound Ionic Molecule Ionic Molecular Compound

20 Molecule Conceptualisation Sketch Nucleic Acid ProteinPolysaccharide DNARNAEnzyme MacromoleculeSmall Molecule Ionic Macromolecular Compound StarchGlycogen mRNAtRNArRNAsnRNA Peptide

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

22 Molecules Revisited Nucleic Acid ProteinPolysaccharide DNARNAEnzyme MacromoleculeSmall Molecule Ionic Macromolecular Compound StarchGlycogen mRNAtRNArRNAsnRNA Peptide Non-Ionic Macromolecular Compound

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

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.

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