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Ontology-Based Computing

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

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Ontology-Based Computing

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  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.

  6. 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, ...

  7. 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.

  8. 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.

  9. 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.

  10. 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!

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

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

  19. 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.

  20. 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

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