1. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester1 OntoGrid OWL Ontology Tutorial Robert Stevens BioHealth Informatics Group Department of Computer Science University of Manchester (including material from Alan Rector’s Semantic Web course )

2. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester2 Speakers ►Robert Stevens – BioInformatics ►Alan Rector – Medical Informatics ►Sean Bechofer – OntoGrid, COHSE ►Matthew Horridge – CO-ODE ►Nick Drummond – CO-ODE ►Jeremy Rogers - OpenGALEN ►Katy Wolstencroft – PhosphaBase, mygrid

3. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester3 Overview (day 1) ►Introduction and Motivation ►Motivation and examples apps ►OWL Language Overview ►The Web Ontology Language and constructs ►Workshop – Protégé-OWL 1 ►Constructing a taxonomy and introduction to reasoning ►Informal Modelling ►Issues when designing an ontology ►Knowledge acquisition methodologies

4. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester4 Overview (day 2) ►Applications ►Architecture and case studies ►Workshop: Protégé-OWL 2 ►Using a reasoner for computing a classification ►Formal Modelling ►Why classify? ►Untangling ►Q&A, Open Discussion ►Feedback

5. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester5 The Car

6. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester6 Coche Automobile Voiture 차 汽车 車 The Car Araba

7. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester7 Hard Work using the Syntactic Web… Find images of Peter Patel-Schneider, Frank van Harmelen and Alan Rector… Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

8. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester8 Impossible (?) using the Syntactic Web… ►Complex queries involving background knowledge ►Find information about “animals that use sonar but are not either bats or dolphins”, e.g., Barn owl ►Locating information in data repositories ►Travel enquiries ►Prices of goods and services ►Results of human genome experiments ►Finding and using “web services” ►Visualise surface interactions between two proteins ►Delegating complex tasks to web “agents” ►Book me a holiday next weekend somewhere warm, not too far away, and where they speak French or English

9. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester9 What is the Problem? Consider a typical web page: Markup consists of: rendering information (e.g., font size and colour) Hyper-links to related content Semantic content is accessible to humans but not (easily) to computers…

10. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester10 What information can we see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7 th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee Tim is the well known inventor of the Web, … Ian Foster Ian is the pioneer of the Grid, the next generation internet …

11. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester11 What information can a machine see…                        

12. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester12 Solution: XML markup with “meaningful” tags?                      …

13. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester13 But What About…                     …

14. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester14 Machine sees…                       

15. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester15 Need to Add “Semantics” ►External agreement on meaning of annotations ►E.g., Dublin Core ►Agree on the meaning of a set of annotation tags ►Problems with this approach ►Inflexible ►Limited number of things can be expressed ►Use Ontologies to specify meaning of annotations ►Ontologies provide a vocabulary of terms ►New terms can be formed by combining existing ones ►Meaning (semantics) of such terms is formally specified ►Can also specify relationships between terms in multiple ontologies

16. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester16 a philosophical discipline—a branch of philosophy that deals with the nature and the organisation of reality ►Science of Being (Aristotle, Metaphysics, IV, 1) ►Tries to answer the questions: What characterizes being? Eventually, what is being? Ontology: Origins and History Ontology in Philosophy

17. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester17 Ontology in Linguistics “Tank“ ReferentForm Stands for Relates to activates Concept [Ogden, Richards, 1923] ?

18. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester18 ►An ontology is an engineering artifact: ►It is constituted by a specific vocabulary used to describe a certain reality, plus ►a set of explicit assumptions regarding the intended meaning of the vocabulary. ►Thus, an ontology describes a formal specification of a certain domain: ►Shared understanding of a domain of interest ►Frmal and machine manipulable model of a domain of interest “An explicit specification of a conceptualisation” [Gruber93] Ontology in Computer Science

19. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester19 Structure of an Ontology Ontologies typically have two distinct components: ►Names for important concepts in the domain ►Elephant is a concept whose members are a kind of animal ►Herbivore is a concept whose members are exactly those animals who eat only plants or parts of plants ►Adult_Elephant is a concept whose members are exactly those elephants whose age is greater than 20 years ►Background knowledge/constraints on the domain ►Adult_Elephants weigh at least 2,000 kg ►All Elephants are either African_Elephants or Indian_Elephants ►No individual can be both a Herbivore and a Carnivore

20. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester20 Ontology Design and Deployment ►Given key role of ontologies in the Semantic Web, it will be essential to provide tools and services to help users: ►Design and maintain high quality ontologies, e.g.: ►Meaningful — all named classes can have instances ►Correct — captured intuitions of domain experts ►Minimally redundant — no unintended synonyms ►Richly axiomatised — (sufficiently) detailed descriptions ►Store (large numbers) of instances of ontology classes, e.g.: Annotations from web pages ►Answer queries over ontology classes and instances, e.g.: ►Find more general/specific classes ►Retrieve annotations/pages matching a given description ►Integrate and align multiple ontologies

21. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester21 Example Ontology

22. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester22 ►Objects/Instances/Individuals ►Elements of the domain of discourse ►Equivalent to constants in FOL ►Types/Classes/Concepts ►Sets of objects sharing certain characteristics ►Equivalent to unary predicates in FOL ►Relations/Properties/Roles ►Sets of pairs (tuples) of objects ►Equivalent to binary predicates in FOL ►Such languages are/can be: ►Well understood ►Formally specified ►(Relatively) easy to use ►Amenable to machine processing Many languages use “object oriented” model based on:

23. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester23 Aside: Set Based Model Theory Many logics (including standard First Order Logic) use a model theory based on Zermelo-Frankel set theory The domain of discourse (i.e., the part of the world being modelled) is represented as a set (often refered as  ) Objects in the world are interpreted as elements of  Classes/concepts (unary predicates) are subsets of  Properties/roles (binary predicates) are subsets of  £  (i.e.,  2 ) Ternary predicates are subsets of  2 etc. The sub-class relationship between classes can be interpreted as set inclusion Doesn’t work for RDF, because in RDF a class (set) can be a member (element) of another class (set) In Z-F set theory, elements of classes are atomic (no structure)

24. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester24 Why it’s hard (1) ►Clash of intuitions ►Subject Matter Experts motivated by custom & practice ►Prototypes & Generalities ►Logicians motivated by logic & computational tractability ►Definitions and Universals ►Transparency & predictability vs Rigour & Completeness ►Neophytes (you?) caught in the muddled middle

25. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester25 Why it’s hard (2) ►Conflation of Models ►Meaning: Correctness of Classification & retrieval ►Indexing:Task of discovery, search, or finding ►Use: Task of data entry, decision support, … ►Acquisition: Task of capturing knowledge ►Assuring quality & managing change ►Quality assurance: Criteria for whether it is ‘correct’ ►Evolution Coping with change ►Regression testingControlling changes & maintaining Quality

26. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester26 Why its hard (3) ►Confusion of terminology and usage ►Religious wars over words and assumptions ►The intersection of ►Linguistics ►Cognitive science ►Software engineering ►Philosophy ►Human Factors ►A jumble of syntaxes

27. BioHealth Informatics Group Ontology Tutorial, © 2005 Univ. of Manchester27 Vocabulary ►“Class”  “Concept”  “Category”  “Type” ►“Instance”  “Individual” ►“Entity”  “object”, Class or individual ►“Property”  “Slot”  “Relation”  “Relationtype”  “Attribute”  Semantic link type”  “Role” ►but be careful about “role” ►Means “property” in DL-speak ►Means “role played” in most ontologies ►E.g. “doctor_role”, “student role” …