Transcript Ontologies (What they are, why you should care)

Ontologies Come of Age
Deborah L. McGuinness
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA 94305
650-723-9770
[email protected]
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
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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)
 Sense disambiguation (e.g., TAP)
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FindUR Architecture
Content to Search:
Research Site
Technical Memorandum
Calendars (Summit 2005, Research)
Yellow Pages (Directory Westfield)
Newspapers (Leader)
Internal Sites (Rapid Prototyping)
AT&T Solutions
Worldnet Customer Care
Medical Information
Search Technology:
User Interface:
Content (Web
Pages or Databases
Classification
CLASSIC Knowledge
Representation System
Search
Engine
Domain
Domain
Knowledge
Knowledge
GUI supporting
browsing
and selection
Results
(standard format)
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Content
Results
(domain specific)
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Verity (and
topic sets)
Collaborative
Topic Set Tool
Verity SearchScript,
Javascript, HTML,
CGI, CLASSIC
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Uses of Ontologies II
 Consistency
Checking
 Completion
 Interoperability
Support
 Support for validation and verification testing (e.g.
http://ksl.stanford.edu/projects/DAML/chimaerajtp-cardinality-test1.daml )
 Configuration support
 Structured, “surgical” comparative customized search
 Generalization / Specialization
 … Foundation for expansion and leverage
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KSL Wine Agent
Semantic Web Integration
Wine Agent receives a meal description and retrieves a selection of
matching wines available on the Web, using an ensemble of
emerging standards and tools:
• DAML+OIL / OWL for representing a domain ontology of
foods, wines, their properties, and relationships between them
• JTP theorem prover for deriving appropriate pairings
• DQL for querying a knowledge base consisting of the above
• Inference Web for explaining and validating the response
• [Web Services for interfacing with vendors]
• Utilities for conducting and caching the above transactions
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Processing
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Given a description of a meal,
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Use DQL to state a premise (the meal) and query the knowledge base
for a suggestion for a wine description or set of instances
Use JTP Theorem Prover to deduce answers (and proofs)
Use Inference Web to explain results (descriptions, instances,
provenance, reasoning engines, etc.)
Access relevant web sites (wine.com, wine commune, …) to access
current information
Use DAML-S for markup and protocol*
http://www.ksl.stanford.edu/projects/wine/explanation.html
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Querying multiple online sources
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A Few Observations about Ontologies
Simple ontologies can be built by non-experts
 Verity’s Topic Editor, Collaborative Topic Builder, GFP, Chimaera,
Protégé, OIL-ED, etc.
Ontologies can be semi-automatically generated
 from crawls of site such as yahoo!, amazon, excite, etc.
 Semi-structured sites can provide starting points
Ontologies are exploding (business pull instead of technology push)
 e-commerce - MySimon, Amazon, Yahoo! Shopping, VerticalNet, …
 Controlled vocabularies (for the web) abound - SIC codes, UMLS,
UNSPSC, Open Directory (DMOZ), Rosetta Net, SUMO
 Business interest expanding – ontology directors, business ontologies
are becoming more complicated (roles, value restrictions, …), VC firm
interested,
 Markup Languages growing XML, RDF, DAML, RuleML, xxML
 “Real” ontologies are becoming more central to applications
 Search companies moving towards them – Yahoo, recently Google
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Implications and Needs for
Ontology-enhanced applications
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Ontology Language Syntax and Semantics (DAML+OIL,
OWL)
Upper Level/Core ontologies for reuse (Cyc, SUMO, CNS
coalition, DAML-S…)
Environments for Creation of Ontologies (Protégé, Sandpiper,
Construct, OilEd, …)
Environments for Maintenance of Ontologies (Chimaera,
OntoBuilder, …)
Reasoning Environments (Cerebra, Fact, JTP, Snark, …)
Distributed explanation support (Inference Web)
Training (Conceptual Modeling, reasoning usage, tutorials –
OWL Guide, Ontologies 101, OWL Tutorial, …)
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Discussion/Conclusion
• Ontologies are exploding; core of many applications
• Business “pull” is driving ontology language tools and
languages
• New generation applications need more expressive ontologies
and more back end reasoning
• New generation users (the general public) need more support
than previous users of KR&R systems
• Distributed ontologies need more support: merging, analysis,
explanation support, incompleteness techniques, versioning,
etc.
• Scale and distribution of the web force mind shift
• Everyone is in the game – US Government (DARPA, NSF,
NIST, ARDA…), EU, W3C, consortiums, business, …
• Consulting and product companies are in the space (not just
academics)
This is THE time for ontology work!!!
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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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<rdfs:Class rdf:ID="BLAND-FISH-COURSE">
<daml:intersectionOf rdf:parseType="daml:collection">
<rdfs:Class rdf:about="#MEAL-COURSE"/>
<daml:Restriction>
<daml:onProperty rdf:resource="#FOOD"/>
<daml:toClass rdf:resource="#BLAND-FISH"/>
</daml:Restriction>
</daml:intersectionOf>
<rdfs:subClassOf rdf:resource="#DRINK-HAS-DELICATE-FLAVOR-RESTRICTION"/>
</rdfs:Class>
<rdfs:Class rdf:ID="BLAND-FISH">
<rdfs:subClassOf rdf:resource="#FISH"/>
<daml:disjointWith rdf:resource="#NON-BLAND-FISH"/>
</rdfs:Class>
<rdf:Description rdf:ID="FLOUNDER">
<rdf:type rdf:resource="#BLAND-FISH"/>
</rdf:Description>
<rdfs:Class rdf:ID="CHARDONNAY">
<rdfs:subClassOf rdf:resource="#WHITE-COLOR-RESTRICTION"/>
<rdfs:subClassOf rdf:resource="#MEDIUM-OR-FULL-BODY-RESTRICTION"/>
<rdfs:subClassOf rdf:resource="#MODERATE-OR-STRONG-FLAVOR-RESTRICTION"/> […]
</rdfs:Class>
<rdf:Description rdf:ID="BANCROFT-CHARDONNAY">
<rdf:type rdf:resource="#CHARDONNAY"/>
<REGION rdf:resource="#NAPA"/>
<MAKER rdf:resource="#BANCROFT"/>
<SUGAR rdf:resource="#DRY"/> […]
</rdf:Description>
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DAML/OWL Language
•Extends vocabulary of
XML and RDF/S
•Rich ontology
representation language
•Language features
chosen for efficient
implementations
Frame Systems
Web Languages
RDF/S
XML
DAML-ONT
DAML+OIL
OWL
OIL
Formal Foundations
Description Logics
FACT, CLASSIC, DLP, …
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Issues
Collaboration among distributed teams
 Interconnectivity with many systems/standards
 Analysis and diagnosis
 Scale
 Versioning
 Security
 Ease of use
 Diverse training levels / user support
 Presentation style
 Lifecycle
 Extensibility
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Services Ontologies
DAML-S http://www.daml.org/services/
 publication references
 ontology specifications
 examples
A few interesting projects using DAML-S:
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MyGrid: (http://mygrid.man.ac.uk)
AgentCities (http://www.agentcities.org)
Services composer
(http://www.mindswap.org/~evren/composer/)
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General Nature of Descriptions
a WINE
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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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SUMO
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Available in KIF (first order logic), DAML, LOOM and
XML
May be used without fee for any purpose (including for
profit)
Mapped by hand to 100,000 synsets of WordNet lexicon
Validated with formal theorem proving
52 publicly released versions created over two years
(approximately 1,000 concepts, 4000 assertions, and 750
rules so far)
Specialized with dozens of free domain ontologies
In use by companies, universities and government around the
world
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Acadmica Sinica – Taiwan, U Arizona, lookwayup.com, NIST etc
Available at http://ontology.teknowledge.com
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Chimaera – A 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; used in HORUS to
support counter-terrorism ontology generation
• 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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Some Pointers
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Ontologies Come of Age Paper:
http://www.ksl.stanford.edu/people/dlm/papers/ontolo
gies-come-of-age-abstract.html
Ontologies and Online Commerce Paper:
http://www.ksl.stanford.edu/people/dlm/papers/ontolo
gies-and-online-commerce-abstract.html
DAML+OIL: http://www.daml.org/
WEBONT: http://www.w3.org/2001/sw/WebOnt/
OWL: http://www.w3.org/TR/owl-features/
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E-Commerce Search
(starting point Forrester Research modified by McGuinness)
Ask Queries
- multiple search interfaces (surgical shoppers, advice seekers,
window shoppers)
- set user expectations (interactive query refinement)
- anticipate anomalies
 Get Answers
- basic information (multiple sorts, filtering, structuring)
- modify results (user defined parameters for refining, user profile
info, narrow query, broaden query, disambiguate query)
- suggest alternatives (suggest other comparable products even
from competitor’s sites)
 Make Decisions
- manipulate results (enable side by side comparison)
- dive deeper (provide additional info, multimedia, other views)
- take action (buy)
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The Need For KB Analysis
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Large-scale knowledge repositories will necessarily contain KBs
produced by multiple authors in multiple settings
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KBs for applications will typically be built by assembling and
extending multiple modular KBs from repositories that may not be
consistent
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KBs developed by multiple authors will frequently
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Express overlapping knowledge in different, possibly contradictory
ways
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Use differing assumptions and styles
For such KBs to be used as building blocks They must be reviewed for appropriateness and “correctness”
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That is, they must be analyzed
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Our KB Analysis Task
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Review KBs that:
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Were developed using differing standards
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May be syntactically but not semantically validated
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May use differing modeling representations
Produce KB logs (in interactive environments)
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Identify provable problems
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Suggest possible problems in style and/or modeling
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Are extensible by being user programmable
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