1. Ontology-Based Computing Kenneth Baclawski Northeastern University and Jarg

2. The Onslaught • Increasingly large amounts of information is becoming accessible electronically. • The information sources are increasingly complicated. • The diversity of types of information source is also increasing. • Technologies are emerging to cope with this onslaught: ontology-based computing.

3. Ontologies • Shared understanding within a community of people • Declarative specification of entities and their relationships with each other • Constraints and rules that permit reasoning within the ontology • Behavior associated with stated or inferred facts

4. Relational Database Schemas • Well established technique for specifying the structure of shared data, not for communication between people or agents • Declarative specification but of tables, not of entities and relationships • Some constraints are expressible but no significant rules (such as inheritance) • No explicit behavior • Standard language is SQL.

5. Object-Oriented Schemas • Emerging technology for communication between software components • Declarative specifications • Constraints and some rules • Several ways to specify behavior • The Unified Modeling Language (UML) is the standard OO modeling language.

7. Logic • Very expressive but very difficult to use. Not designed for communication. • Most logical languages are not based on entities and relationships. • Very powerful inferencing capabilities. • Do not usually have any associated behavior. • Many examples: Prolog, KIF, Slang, ...

8. XML DTDs and XML Schema • Defines a hierarchical document type. XML Schema defines data types. Designed for communication over the Web. • Good support for entities and hierarchical relationships; awkward for others. • Constraints can be imposed on the hierarchical structure and on data types. • Behavior can be specified procedurally.

9. Knowledge Representations • Very well developed branch of AI. Many tools, but mostly academic. Not yet used for communication over the Web. • Powerful language for specifying entities and their relationships. • Most are linked with inference engines. • Behavior is typically handled in an ad hoc manner.

10. RDF and DAML • Resource Description Framework (RDF) is a knowledge representation language represented in XML. It is a WWW Consortium Recommendation. • The DARPA Agent Markup Language (DAML) is an extension of RDF to serve as the basis for ontology-based computing over the Web: the Semantic Web.

11. type type type Person Fish range type type type owns domain Wendy Wanda owns Ontological Reasoning in RDF Property Class Type constraint violation: The range of owns is Fish. Mermaid? OR There is no inconsistency: Wanda is a fish!

12. type type type domain Student College majors range subClassOf type onProperty type 1 Engineering maxCardinality equivalentTo majors type type Arts & Sciences George majors DAML Property Class Restriction Cardinality constraint violation: George can’t have two majors OR There is no inconsistency: Engineering = Arts & Sciences

13. Representing information • Relational database: records • OO database: objects and links • Logic: facts • XML: documents • Knowledge Representations: annotations • All of these are graph structures: entities related to other entities by relationships.

14. Where is the meaning? • Databases: select-project-join queries • Logic: rules determined by unification • XML: XSLT patterns • Knowledge Representations: templates • All of these are forms of graph matching. The units of meaning are small connected subgraphs that I call motifs.

15. Ontology Infrastructure Simply introducing a language is not enough. There must be an infrastructure to support ontology-based computing, including: • Ontology development tools • Content creation systems • Storage and retrieval systems • Ontology reasoning, mediation, ... • Integration with applications

16. Ontology Development • Ontologies can be developed using graphical tools specifically for ontologies or by adapting existing tools such as CASE tools. • Testing ontologies is not easy because they include constraints and inference rules. • Ontology testing is analogous to type checking in programming languages.

17. Content Creation • Databases: Data warehousing technology • Text: Natural Language Processing (NLP) • Image processing • Direct creation of content • No matter how the content is created it must be tested using consistency checking.

18. Storage and Retrieval • Scaling up will require high-performance, distributed storage and indexing technology. • The natural units for indexing are the motifs (precomputed joins), but the number of motifs is large. • Jarg Corporation has developed a scalable, high-performance indexing technology for ontology-based knowledge representations.

19. NLP Knowledge Representation fragmentation Knowledge Fragments Matching Documents Distributed Index Engine Knowledge Motifs fragmentation NLP Knowledge Representation Jarg Architecture Document Query

20. Conclusion • Ontology-based computing is emerging as a natural evolution of existing technologies to cope with the information onslaught. • Ontology-based technology must be scalable if it is to contribute to the solution rather than add to the problem. • Consistency checking is important for the development of ontologies and content.

21. Bibliography • Semantic Web: www.w3.org/2001/sw • Ontologies: www.ontology.org • Unified Modeling Language: www.omg.org/uml • Knowledge Interchange Format: logic.stanford.edu/kif • Specware and Slang: www.kestrel.edu • XML and XML Schema: www.w3.org/xml • RDF and RDFS: www.w3.org/rdf • DAML: www.daml.org • Notation 3: www.w3.org/DesignIssues/Notation3.html • Consistency checking: vis.home.mindspring.com • Jarg Knowledge Engine: www.jarg.com