Ontology-Based Computing

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
(No Transcript)
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
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
DAML
Property
Class
Restriction
Cardinality constraint violation George cant
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
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/k
  if
• 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