Transcript Ontologies and the Semantic Web

Ontologies and the
Semantic Web
Deborah L. McGuinness
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA 94305
650-723-9770
[email protected]
Outline
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The Web is moving to a Semantic Web
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Ontologies
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What is it
How can a web with semantics be used
What are they
How can they be used
Second Session
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How can I get started (a look at requirements, languages, ad tools)
Discussion in an example domain
Session 1: Based loosely on Ontologies Come of Age.
Session 2: Based loosely on Ontology Engineering 101, OWL Overview,
and OWL Guide, How and When to Live with a Kl-ONE-like System
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Yesterday: Rich Information Source for
Human Manipulation/Interpretation
Human
Human
Human
Human
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“I know what was input”
The web knows what text was input (and is great at
information dissemination) but does little
interpretation, manipulation, integration, and
action.
Analogous to a new assistant who is thorough yet
lacks common sense, context, adaptability, and the
ability to interpret for you
Some people view this as the “syntactic web”
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Moving to… Rich Information Source for
Agent Manipulation/Interpretation
Human
Agent
Agent
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“I know what was meant”
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Understand term meaning and user background
Interoperable (can translate between applications)
Programmable (thus agent friendly and 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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Scientific American, May 2001:
Having a web that knows “what you want” or “what you mean” is
accomplished by semantics…. specifically using semantic
annotation on web resources
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Semantic Enablers
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Languages for representing term meaning – used to
build ontologies
Tools for generating, maintaining, and evolving
ontologies
Tools for reasoning with and using semantically
enhanced applications
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Layer Cake Foundation
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What is an Ontology?
Catalog/
ID
Thesauri
“narrower
term”
relation
Terms/
glossary
Frames General
Formal
is-a (properties) Logical
constraints
Informal
is-a
Formal
instance
Disjointness,
Value Inverse, partRestrs. of…
*based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann
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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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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 and Navigation Support
 Expectation setting (left side of many web pages)
 “Umbrella” Upper Level Structures (for extension)
 Browsing support (tagged structures such as Yahoo!)
 Search support (query expansion approaches such as
FindUR, e-Cyc)
 Sense disambiguation
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Example Search Application
Research exemplar of many “smart” search
applications
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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.
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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<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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Processing
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Given a description of a meal,
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Use OWL-QL/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 to deduce answers (and proofs)
Use Inference Web to explain results (descriptions, instances,
provenance, reasoning engines, etc.)
Access relevant web sites (wine.com, …) to access current
information
Use OWL-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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Observations from the Wine Agent
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Background knowledge is reasonably simple and built in OWL (includes
foods and wine and pairing information similar to the OWL Guide, Ontology
Engineering 101, CLASSIC Tutorial, …)
Background knowledge can be used for simple query expansion over wine
sources to retrieve for example documents about red wines (including
zinfandel, syrah, …)
Background knowledge used to interact with structured queries such as those
possible on wine.com
Constraints allows a reasoner like JTP to infer consequences of the premises
and query.
Explanation system (Inference Web) can provide provenance information
such as information on the knowledge source (McGuinness’ wine ontology)
and data sources (such as wine.com)
Services work could allow automatic “matchmaking” instead of hand coded
linkages with web resources
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Semantically Driven Information
Rich Task Architecture: KANI
Keyword
Search
The World
Semantic
Search
Knowledge
Browsing &
Selection
Relevant
Knowledge
Identification
(TAP)
Selection
Knowledge
Interaction
Corpus
Inference
Web
Knowledge
Extraction
Explanation
Generation
Knowledge
Transfer
Extracted
Knowledge
DB
Working
KB
Hypothesis
Modeling &
Testing
Shared
Reasoning
Devil’s
Advocate
Analysis
Management
Entities
Background
KB
Ontology
Legend
Models
Data
System
Component
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System
Service
User Interface
Feature
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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 - 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,OWL,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 and Domain ontologies for reuse (Cyc, SUMO,
CNS coalition, DAML-S… UMLS, GO, …)
Environments for Creation of Ontologies (Protégé, Sandpiper,
Construct, OilEd, …)
Environments for Maintenance of Ontologies (Chimaera,
OntoBuilder, …)
Reasoning Environments (Cerebra, Fact, JTP, Snark, …)
Environment support for Explanation (Inference Web, …)
Training (Conceptual Modeling, reasoning usage, tutorials –
OWL Guide, Ontologies 101, OWL Tutorial, …)
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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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W3C Web Ontology Working Group
and OWL
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WebOnt is part of W3C Semantic Web Activity aimed
at extending meta-data efforts
Begins from DAML+OIL W3C Note in 2001
Produces OWL which reached recommendation status
in February 2004
OWL receives testimonials, news coverage, and usage
escalates
Best Practices Working Group
Companies such as Network Inference, Sandpiper, etc
support OWL as do open source and research orgs
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visual ontology modeler™ (VOM) 1.x
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CONSTRUCT*
Rapid Modeling, Visual Editing
Provides graphical and text
environment for editing
Exports to OWL; Processed by Cerebra Server
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* All Rights Reserved by Network Inference Inc
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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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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
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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Inference Web
Framework for explaining question answering tasks by abstracting,
storing, exchanging, combining, annotating, filtering,
segmenting, comparing, and rendering proofs and proof
fragments provided by question answerers
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IW’s Proof Markup Language (PML) is an interlingua for
proof interchange
IWBase is a distributed repository of meta-information related
to proofs and their explanations
IW Browser is an IW tool for displaying PML documents
containing proofs and explanations (possibly from multiple
inference engines)
IW Explainer is an IW tool for abstracting proofs into more
understandable formats
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Discussion
• The Semantic Web is arriving – annotation information is
emerging – may be hand done or simple meta tags such as
date, author, etc.
• 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
• 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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Conclusion/Next
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Languages are stable, endorsed, and available –
e.g., OWL from W3C
Tools are stable, although less standardized,
available open source and commercially – e.g.,
Protégé, Sandpiper, Network Inference, …
Next session will introduce how to get started
identifying requirements, language overview, and
tool support with an example
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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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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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