Ontologies: What you should know
and why you might care
Deborah McGuinness
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA USA
[email protected]
http://www.ksl.stanford.edu/people/dlm
McGuinness
COGNA October 3, 2003
What is an Ontology?
Catalog/
ID
Thesauri
“narrower
term”
relation
Terms/
glossary
Formal
taxonomy
Term
Hierarchy
(e.g.
Yahoo!)
General
Frames Description
(properties) Logics
Formal
instance
Value General
Restrs. Logic
*based on AAAI ’99 Ontologies panel – Gruninger, Lehmann, McGuinness, Uschold, Welty
Updated by McGuinness, additional input from Gruninger, Uschold, and Rockmore
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General Nature of Descriptions
class
superclass
number/card
restrictions
Roles/
properties
value
restrictions
McGuinness
a WINE
a LIQUID
a POTABLE
general categories
grape: chardonnay, ... [>= 1]
sugar-content: dry, sweet, off-dry
color: red, white, rose
price: a PRICE
winery: a WINERY
structured
components
grape dictates color (modulo skin)
harvest time and sugar are related
interconnections
between parts
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Some uses of Ontologies
Simple ontologies (taxonomies) provide:
• Controlled shared vocabulary (search engines,
authors, users, databases, programs/agents all speak
same language)
• Site Organization, Navigation Support, Expectation
setting
• “Umbrella” Upper Level Structures (for extension
e.g., UNSPSC)
• Browsing support (tagged structures such as Yahoo!)
• Search support (query expansion approaches such as
FindUR, e-Cyc; structured search)
• Sense disambiguation (e.g., TAP)
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Semantic Web Vision
Today’s web enriched with information encoding term
meaning enabling applications that are:
• Able to understand term meaning and user background
• Interoperable (can translate between applications and
vocabularies)
• Programmable (thus agent operational)
• Explainable (thus maintains context and can adapt)
• Capable of filtering (thus limiting display and human
intervention requirements)
• Capable of executing services
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Semantic Enablers
• Languages for representing terms in vocabularies
• Tools for generating, maintaining, and evolving
ontologies
• Tools for reasoning with and using semantically
enhanced applications
Facilitated by W3C, Govt - DARPA, ARDA, NSF,
NIST, EU, …
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DARPA’s DAML/
W3C’s OWL
Language
Web Languages
RDF/S
XML
DAML-ONT
DAML+OIL
(OWL)
Frame Systems
OIL
Formal Foundations
Description Logics
FACT, CLASSIC, DLP, …
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Ontology Resources…
•
•
•
•
•
Upper Level Ontologies- UNSPSC, SUMO, OpenCyc, OpenDirectory, TAP, …
Specialized Ontologies (Many beyond just GML)
– Geography Ontology CIA World Fact Book geographic regions; WFB climate
data interpreted using Koeppen Climate Classification systemwww.fao.org/WAICENT/FAOINFO/sustdev/EIdirect/climate/EIsp0002.htm 3. Sea
Level definitions - www.pol.ac.uk/psmsl/puscience/index.html
– Geography Ontology - geographical ontology & theory in FOL capable of
accessing and utilizing information from a variety of agents, including Alexandria
Digital Library Gazetteer, TerraVision, the CIA World Factbook, Teknowledge's
ASCS, and Landsat and GDACC satellite data repositories. Using axiomatic
characterizations of these agents’ capabilities, in conjunction with SNARK's
procedural-attachment mechanism and the OAA agent library, the combined
theory is capable of finding answers that must be inferred from more than one of
these sources because no one source has the entire answer
Ontology Libraries
– http://www.daml.org/ontologies/
– http://www.ksl.stanford.edu/ontolingua
“Advisory” bodies - Semantic Web Science Foundation, NIST, Ontology.org
Ontology Consultants
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Ontology Tools
Tools developing: http://www.daml.org/tools/ and
http://www.w3.org/2001/sw/WebOnt/impls#Implementations
Annotation
Browser
Crawler
Editor
Graph Visualizer
Transformation
Validator
Importer
Inference Engine
Ontology Translation
Persistence
Query Tools
RDMS Mapping
Report Generation
Search
Ontology Analyzer
Ontology Editor
Merging
Many are in research labs, but companies emerging and lasting…
Network Inference, Sandpiper, Ontoprise, AppliedSemantics, Sentius, ….
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Conclusion/Discussion
• Ontologies are taking off in terms of languages,
tools, environments, and applications
• Rich representation languages exist for
representing taxonomies, thesauri, and beyond.
• Transition paths exist from standard languages
such as XML to other web standards like RDF and
OWL
• Ontology toolkits for ontology building, evolution,
merging, etc. exist today and are growing quickly
(academics, government, and industry)
• Ontology libraries exist and are worth considering
for leverage, connections, and merging
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Pointers
Selected Papers:
- McGuinness. Ontologies come of age, 2003
- Das, Wei, McGuinness, Industrial Strength Ontology Evolution Environments, 2002.
- Kendall, Dutra, McGuinness. Towards a Commercial Strength Ontology Development Environment, 2002.
- McGuinness Description Logics Emerge from Ivory Towers, 2001.
- McGuinness. Ontologies and Online Commerce, 2001.
- McGuinness. Conceptual Modeling for Distributed Ontology Environments, 2000.
- McGuinness, Fikes, Rice, Wilder. An Environment for Merging and Testing Large Ontologies, 2000.
- Brachman, Borgida, McGuinness, Patel-Schneider. Knowledge Representation meets Reality, 1999.
- McGuinness. Ontological Issues for Knowledge-Enhanced Search, 1998.
- McGuinness and Wright. Conceptual Modeling for Configuration, 1998.
Selected Tutorials:
-Smith, Welty, McGuinness. OWL Web Ontology Language Guide, 2003.
-Noy, McGuinness. Ontology Development 101: A Guide to Creating your First Ontology. 2001.
- Brachman, McGuinness, Resnick, Borgida. How and When to Use a KL-ONE-like System, 1991.
Languages, Environments, Software:
- OWL - http://www.w3.org/TR/owl-features/ , http://www.w3.org/TR/owl-guide/
- DAML+OIL: http://www.daml.org/
- Inference Web - http://www.ksl.stanford.edu/software/iw/
- Chimaera - http://www.ksl.stanford.edu/software/chimaera/
- FindUR - http://www.research.att.com/people/~dlm/findur/
- TAP – http://tap.stanford.edu/
- DQL - http://www.ksl.stanford.edu/projects/dql/
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EXTRAS
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OWL Lite Features
•
•
•
•
•
•
RDF Schema Features
– Class, rdfs:subClassOf , Individual
– rdf:Property, rdfs:subPropertyOf
– rdfs:domain , rdfs:range
Equality and Inequality
– sameClassAs , samePropertyAs , sameIndividualAs
– differentIndividualFrom
Restricted Cardinality
– minCardinality, maxCardinality (restricted to 0 or 1)
– cardinality (restricted to 0 or 1)
Property Characteristics
– inverseOf , TransitiveProperty , SymmetricProperty
– FunctionalProperty(unique) , InverseFunctionalProperty
– allValuesFrom, someValuesFrom (universal and existential local range
restrictions)
Datatypes
– Following the decisions of RDF Core.
Header Information
– imports , Dublin Core Metadata , versionInfo
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OWL Features
•
Class Axioms
–
–
–
–
•
oneOf (enumerated classes)
disjointWith
sameClassAs applied to class expressions
rdfs:subClassOf applied to class expressions
Boolean Combinations of Class Expressions
– unionOf
– intersectionOf
– complementOf
•
Arbitrary Cardinality
– minCardinality
– maxCardinality
– cardinality
•
Filler Information
– hasValue Descriptions can include specific value information
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Chimaera: Ontology Environment
Tool
An interactive web-based tool aimed at supporting:
•Ontology analysis (correctness, completeness, style, …)
•Merging of ontological terms from varied sources
•Maintaining ontologies over time
•Validation of input
• Features: multiple I/O languages, loading and merging into multiple
namespaces, collaborative distributed environment support, integrated
browsing/editing environment, extensible diagnostic rule language
• Used in commercial and academic environments, basis of some
commercial re-implementations (Ontobuilder/Ontoserver, …)
• Available as a hosted service from www-ksl-svc.stanford.edu
• Information:
www.ksl.stanford.edu/software/chimaera
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Layer Cake Foundation
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XML
• World Wide Web Consortium (W3C) standard
• Provides important solution to syntax problem and
simple semantics and schemas:
<SSN>555-17-1234</SSN>
• Now we can describe the meaning of words
• Many applications of XML appearing:
– Geographic Markup Language (GML)
– Extensible rights Markup Language (XrML)
– Chemical Markup Language (CML)
Problem: Limited semantics, limited ontology creation
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DARPA Agent Markup Language
•
•
•
•
http://www.daml.org/about.html
Extends the vocabulary of XML and RDF/S
Provides rich ontology representation language
Language features chosen so language may have
efficient implementations
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DAML+OIL -> W3C
• W3C Webont working group formed with
DAML+OIL submission as starting point
http://www.w3.org/Submission/2001/12/
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WEBONT participation….
• Includes over 50 members from over 30 organizations.
– Industry including:
• Large companies such as Daimler Chrysler, EDS, Fujitsu, HP, IBM, Intel,
Lucent, Nokia, Philips Electronics, Sun, Unisys, …
• Newer/smaller companies such as IVIS Group, Network Inference, Stilo
Technology, Unicorn Solutions, …
– Government and Not-For-Profits:
• Defense Information Systems Agency, Interoperability Technology
Association for Information Processing, Japan (INTAP) , Intelink Mgt Office,
Mitre, …
– Universities and Research Centers:
• University of Bristol, University of Maryland, University of Southamptom,
Stanford University, …
• DFKI (German Research Center for Artificial Intelligence),
Forschungszentrum Informatik
– Invited Experts
• Well-known academics from non-W3C members
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Simple Ontology-Enhanced Apps
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Today: Rich Information Source for
Human Manipulation/Interpretation
Human
Human
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“I know what was input”
• Global documents and terms indexed and available for search
• Search engine interfaces
• Entire documents retrieved according to relevance (instead of
answers)
• Human input, review, assimilation, integration, action, etc.
• Special purpose interfaces required for user friendly applications
The web knows what was input but does little interpretation,
manipulation, integration, and action.
Analogous to a new assistant who is thorough yet lacks common
sense, context, and adaptability
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Tomorrow: Rich Information Source
for Agent Manipulation/Interpretation
Human
Agent
Agent
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“I know what was meant”
•
•
•
•
•
Understand term meaning and user background
Interoperable (can translate between applications)
Programmable (thus agent operational)
Explainable (thus maintains context and can adapt)
Capable of filtering (thus limiting display and
human intervention requirements)
• Capable of executing services
McGuinness
COGNA October 3, 2003
Contact Information
[email protected]
www.ksl.stanford.edu/people/dlm
McGuinness
COGNA October 3, 2003
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Web Ontology Language - OWL