1. My Experience in Building Ontology-driven Applications Harry Chen eBiquity Group Meeting February 9, 2004

2. The Big Objective Share my experience in using OWL & related tools when prototyping CoBrA Bring your attentions to critical research issues in building ontology-driven apps. Initiate discussions on future project ideas

3. Outline The role of ontologies in PerCom Steps in building ontology-driven apps Different kinds of useful ontology tools Pitfalls in the development process Future project ideas

4. Where do we use ontologies? Pervasive computing is great!

5. Key Uses of Ontologies Knowledge sharing Context Broker shares context knowledge with devices and agents Ontology based reasoning Context Broker infers the properties associated with a person’s location context Policy language specification User defines privacy policies to restrict the access to their contextual information Service discovery & composition Display my presentation on that wall

6. What do we have in mind? Prototyping PerCom systems Devices, services & agents Distributed systems (the Web, mobile devices, desktop computers etc.) The ontology language The Web Ontology Language OWL Things communicate with information expressed in RDF/XML, N3, N-Triples

7. Ontology Development Cycle Determine what you want do model Check if existing ontologies can be reused Define Classes & Properties Define test cases to validate your ontology Test ontology in your domain application Ontology Develop 101: A Guide to Creating Your First Ontolgy Natalya F. Noy & Deborah L. McGuinness The Perfect World Scenario & The World is Not Perfect!

8. My Development Cycle Determine what you want do model with use cases Study closely related ontologies (structure, vocab) Draft your ontologies by “borrowing” from other ontologies Define “instants” of your ontology; test with the use cases Prototype your applications using the defined “instants” Know your application is important; Know the use cases of your application is even more important

9. Evaluating Your Ontology How do you know if your ontology is good? If it works well in your application What doesn’t matter? The size of your ontology The complexity of your ontology The vocabularies in your ontology

10. About Ontology Development Ontology development is typically harder than software development No good metrics to evaluate ontology A program is good if it runs, and an ontology is good only if it works with a running program Typically you write programs after you have developed the ontologies Kind of like the chicken-and-egg question

11. What You Should Know Before you start to define ontologies Know how the ontologies will be used Is it for supporting knowledge sharing? Is it for supporting reasoning? If it’s for supporting reasoning Know how they’ll “fit” into the reasoner DL classification Logic inferences that is beyond the OWL model

12. Useful Ontology Tools Ontology editors Program API libraries Ontology reasoners Ontology validators Ontology visualizers Ontology management tools

13. Ontology Editors EditorProsCons Emacs + SGML No installation is required; code format No ontology validation; a lot of typing Emacs + OWL Mode Syntax check; code format Requires some amount of hacking; a lot of typing Protégé + OWL Plug-in Easy installation; GUI editor; little XML editing; OWL validation; can suggest ontology re- structure using the Racer classifier No control over how the ont-document will look like; has some learning curve; some editing operations are slow; changes very often

15. Program API Libraries Jena Toolkit (for Java) Well documented and designed Supports different types of RDF storage In-memory, persistent (RDBMS, plain files) Has I/O support for RDF/XML, N3 & N-Triple syntax Built-in Java rule-based reasoner (RDF-S & OWL) You can add your reasoning rules! Known issues: OWL reasoning is limited and has performance issues; multi-threaded access is unstable; some API’s are confusing

16. Ontology Reasoner Reasoners for the OWL data model F-OWL (Flora2/XSB) -- make friend with Youyong now! FaCT Jena2 -- some performance issues OWLP (RDF/XML, Xquery) -- weird. Euler (python) -- a good learning tool. …

17. Other Reasoners For reasoning beyond the OWL data model, you need something different Mix Prolog with F-OWL (TAGA, REI) Mix Jess with your own OWL rules (CoBrA) Mix Jena2 with your own Jena inference rules (both forward & backward rules are okay) (CoBrA)

18. OWL Validators Validating the RDF syntax W3C RDF validation service Validating the OWL syntax BBN’s OWL validator Validating the species of your OWL ontology (OWL Lite, DL, or Full). OWL Ontology Valiator No ontology is a good ontology if it doesn’t validate. -- anonymous

19. Other Emerging Tools Visualizers ObjectView RDF Validator Protégé + OWL Plug-in Management tool IBM SNOBASE (kind of DBMS for ontologies)

20. Pitfalls … in … ontology dev. (1) You have defined too many useless vocabularies Somehow we enjoy creating new vocabularies just because we can! Not knowing how your ontologies will be used in the application Not having use case to drive your development

21. Pitfalls … in … ontology dev. (2) When some property can be defined to exploit XSD data types, you choose to use nested ObjectProperty constructs You choose to use nested date/time classes and properties instead of xsd:dataTime To represent latitude/longitude coordinates, you choose to define individual class and properties of “degrees”, “minutes” & “seconds” when you can use xsd:string.

22. Pitfalls … in … ontology dev. (3) You think too much about Java classes when you define OWL classes When defining the method (property) of a Java class, you specify its name & behavior When defining the property of an OWL class, you specify its name and restrictions An OWL class with properties that only have defined names and no restrictions is not very useful.

23. Pitfalls … in … ontology dev. (4) Developing ontologies without knowing exactly how they will be use Ontologies are here to support the function of your application Ontologies on a paper is not very useful to a programmer.

24. Issues & Answers (maybe) Lacking a light-weight and flexible reasoning Doesn’t need to be very powerful, but it should be highly customizable. Better if it is in pure Java Something that I can use to prototype FOAF use cases on a PDA.

25. Issues & Answers (maybe) Lacking commercial strength editors and management tools Not much we can do in our group (I think) But we should try to use different editors and give feedbacks to community +

26. Issues & Answers (maybe) The learning curve for an average programmer to start building ontology- driven applications is still too high. They must understand XML, XSD The difference between XML, RDF/RDF-S & OWL Knowledge representation Ontology API Rule-based inferences for building inference rules …

27. Concluding Remarks Ontologies are can help us to build more intelligent and interoperable pervasive computing applications if we learn how to use them right. The magic in developing good ontologies is to “practice more and often”, and know how your applications will use the ontologies. Emacs still the best ontology editor We need a light-weight and flexible ontology reasoner.