Next Generation Scientific
Digital Libraries
(or The Semantic Web and Digital
Libraries as Knowledge Systems)
Deborah McGuinness
Co-Director Knowledge Systems, Artificial Intelligence Laboratory
Stanford University
dlm@ksl.stanford.edu
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Outline
• Introduction
• The Semantic Web, Ontologies, and the
Ontology Web Language
• Selected Technical Benefits of Semantic
Technologies
• Discussion and Directions
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Semantic Web Perspectives
• The Semantic Web means different things to different
people. It is multi-dimensional
– Distributed data access
– Inference
– Data Integration
– Logic
– Services
– Search (based on term meaning)
– Configuration
– Agents
–…
• Different users value these dimensions differently
• Theme: Machine-operational declarative specification of
the meaning of terms
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Semantic
Web
Layers
Ontology Level
– Languages (CLASSIC, DAML-ONT, DAML+OIL, OWL, …)
– Environments (FindUR, Chimaera, OntoBuilder/Server, Sandpiper
Tools, …)
– Standards (NAPLPS, …, W3C’s WebOnt, W3C’s Semantic Web
Best Practices, EU/US Joint Committee, OMG ODM, …
Rules
– SWRL (previously CLASSIC Rules, explanation environment,
extensibility issues, contracts, …)
Logic
– Description Logics
Proof
– PML, Inference Web Services and
Infrastructure
Trust
– IWTrust, Policy encodings, …
http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.html
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Semantic Web Statements
• The Semantic Web is made up of individual
statements
subject
predicate
object
• The subject and predicate are Uniform Resource
Identifiers (URIs) – the object can be a URI or an
optionally typed literal value
worksFor
collaboratesWith
#Peter
worksFor
#Stanford
#Deborah
worksFor
#NCAR
surname
surname
“Fox”
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#McGuinness
Assoc
“McGuinness”
Ontology Spectrum
Catalog/
ID
Thesauri
“narrower
term”
relation
Terms/
glossary
Informal
is-a
Frames General
Formal
is-a (properties) Logical
constraints
Formal
instance
Disjointness,
Value Inverse, partRestrs. of…
Ontology languages such as DAML+OIL, OWL can be used to encode the spectrum
Originally from AAAI 1999- Ontologies Panel – updated by McGuinness
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General Nature of
Descriptions
class
superclass
number/card
restrictions
Roles/
properties
value
restrictions
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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DAML/OWL Language
Web Languages
XML
•Extends vocabulary of
RDF/S
XML and RDF/S
DAML-ONT
•Rich ontology
representation language
DAML+OIL
•Language features
OWL
OIL
chosen for efficient
Formal Foundations
implementations
Frame Systems
Description Logics
FACT, CLASSIC, DLP, …
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Selected Technical Benefits
1.
2.
3.
4.
5.
6.
7.
8.
Integrating Multiple Data Sources
Semantic Drill Down / Focused Perusal
Statements about Statements
Inference
Translation
Smart (Focused) Search
Smarter Search … Configuration
Proof
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1: Integrating Multiple Data
Sources
• The Semantic Web lets
us merge statements
from different sources
• The RDF Graph Model
allows programs to use
data uniformly regardless
of the source
• Figuring out where to find
such data is a motivator
for Semantic Web
Services
#US
currency
name
“United
States”
#USD
telephoneCode
“1”
Different line & text colors
represent different data sources
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2: Drill Down /Focused Perusal
• The Semantic Web uses
Uniform Resource Identifiers …#Deborah
(URIs) to name things
• These can typically be
resolved to get more
information about the resource
worksFor
• This essentially creates a web
of data analogous to the web
of text created by the World
Internet
Wide Web
• Ontologies are represented
using the same structure as
...#McGuinness
content
– We can resolve class and
property URIs to learn about
the ontology
Assoc
…#California
locatedIn
...#Stanford
type
type
...#Company …#University
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3: Statements about Statements
• The Semantic Web
allows us to make
statements about
statements
– Timestamps
– Provenance / Lineage
– Authoritativeness /
Probability / Uncertainty
– Security classification
– …
#Estimate
#US
type
population
year
2003
290342554
• This is an unsung virtue
of the Semantic Web
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From CIA World Factbook
4: Inference
• The formal foundations of
the Semantic Web allow
us to infer additional
(implicit) statements that
are not explicitly made
• Unambiguous semantics
allow question answerers
to infer that objects are
the same, objects are
related, objects have
certain restrictions, …
• SWRL allows us to make
additional inferences
beyond those provided by
the ontology
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sibling
#Joe
hasBrother
#Louise
sibling
daughterOf
hasUncle
hasMother
hasChild
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#Deborah
5: Translation
• While encouraging sharing,
the Semantic Web allows
multiple URIs to refer to the
same thing
• There are multiple levels of
mapping
–
–
–
–
Classes
Properties
Instances
Ontologies
#car
type
ont1:country
ont1:Car
fips:UK
#car
type
• OWL supports equivalence
and specialization; SWRL
allows more complex
mappings
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ont2:Vehicle
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ont2:country
iso:GB
6: Smart (Focused) Search
• The Semantic Web
associates 1 or more
classes with each
object
• We can use ontologies
to enhance search by:
–
–
–
–
Query expansion
Sense disambiguation
Type with restrictions
….
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7: Smarter Search / Configuration
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KSL Wine Agent
Semantic Web Integration Example
Uses emerging web standards to enable smart web
applications
Given a meal description
•Deborah’s Specialty
Describe matching wines
•White, Dry, Full bodied…
Retrieve some specific options from web
•Forman Chardonnay from DLM’s cellar, ThreeSteps from wine.com, ….
•
Info: http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/
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KSL Wine Agent
Semantic Web Integration
Technology
OWL
for representing a domain ontology of foods, wines, their properties,
and relationships between them
JTP theorem prover
for deriving appropriate pairings
DQL/OWL QL
for querying a knowledge base
Inference Web
for explaining and validating answers
(descriptions or instances)
Web Services
for interfacing with vendors
Connections to online web agents/information services
Utilities for conducting and caching the above transactions
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8: Proof
• The logical foundations
of the Semantic Web
allow us to construct
proofs that can be used
to improve transparency,
understanding, and trust
• Proof and Trust are ongoing research areas for
the Semantic Web: e.g.,
See PML and Inference
Web
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#W3C
hasMember
#Acme
hasEmployee
#Bob
“Employees of member companies
can access W3C’s content”
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Scientific Digital Libraries
Scientists should be able to access a global, distributed
knowledge base of scientific data that:
• appears to be integrated
• appears to be locally available
• is easy to search
But… data is obtained by multiple instruments, using
various protocols, in differing (possibly unfamiliar)
vocabularies, using (sometimes unstated)
assumptions, with inconsistent (or non-existent)
meta-data. It may be inconsistent, incomplete,
evolving, and distributed
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Future Science Digital Libraries
Repositories of data with markup and provenance that
enables…
- sharing data AND tools with distributed colleagues
- understanding assumptions, constraints, and enough
information to determine applicability and reuse
- research data and experiment composition and
dependence
- consistency and validation checking and more…
Current and future repositories are poised to change the
nature of how science is done by supporting interoperability
and sharing at new levels.
Projects like the Virtual Solar Terrestrial Observatory, GEON,
etc. use semantic web technology to enable next generation
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digital scientific libraries
Conclusion
• Semantic Web Languages and Tools are ready for use
(OWL, OWL-S, Cerebra, Sandpiper, …)
• Predecessor technology (description logics etc.) have
been in use for decades
• Current ontologies and tools being used in science:
–
–
–
–
–
–
–
Gene Ontology (GO)
NCI and UMLS
SWEET (Semantic Web for Earth and Environmental Terminology )
Immune Epitope DataBase
GEON
Virtual Solar Terrestrial Observatory
…
• The time is NOW to work together towards next
generation semantically-enabled interoperable systems
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Resources
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.
Selected Tutorials:
-Smith, Welty, McGuinness. OWL Web Ontology Language Guide, 2004.
-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/
- Inference Web - http://www.ksl.stanford.edu/software/iw/
- Wine Agent - http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/
- Chimaera - http://www.ksl.stanford.edu/software/chimaera/
- FindUR - http://www.research.att.com/people/~dlm/findur/
- TAP – http://tap.stanford.edu/
- OWL-QL - http://www.ksl.stanford.edu/projects/owl-ql/
- Cerebra (formerly Network Inference) – http://www.cerebra.com
- Sandpiper Software – http://www.sandsoft.com
- Virtual Solar Terrestrial Observatory - http://vsto.hao.ucar.edu/
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EXTRAS
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OWL
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OWL Sublanguages
• OWL Lite supports users primarily needing a classification hierarchy
and simple constraint features. (For example, while it supports
cardinality constraints, it only permits cardinality values of 0 or 1. It
should be simpler to provide tool support for OWL Lite than its more
expressive relatives, and provides a quick migration path for thesauri
and other taxonomies.)
• OWL DL supports users who need maximum expressiveness while
their reasoning systems maintain computational completeness (all
conclusions are guaranteed to be computed) and decidability (all
computations will finish in finite time). OWL DL includes all OWL
language constructs, but they can be used only under certain
restrictions (for example, while a class may be a subclass of many
classes, a class cannot be an instance of another class). OWL DL is
named for its correspondence with description logics.
• OWL Full supports users who want maximum expressiveness and
the syntactic freedom of RDF with no computational guarantees. For
example, in OWL Full a class can be treated simultaneously as a
collection of individuals and as an individual in its own right. OWL Full
allows an ontology to augment the meaning of the pre-defined (RDF
or OWL) vocabulary. It is unlikely that any complete and efficient
reasoner will be able to support every feature of OWL Full.
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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
– equivalentClass , equivalentProperty , sameAs
– differentFrom
– AllDifferent , distinctMembers
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
equivalentClass 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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OWL Lite and OWL
• Overview:
http://www.w3.org/TR/owl-features/
• Guide:
http://www.w3.org/TR/owl-guide/
• Reference:
http://www.w3.org/TR/owl-ref/
• Semantics and Abstract Syntax:
http://www.w3.org/TR/owl-absyn/
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Virtual Solar Terrestrial
Observatories
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Background
Scientists should be able to access a global,
distributed knowledge base of scientific data
that:
• appears to be integrated
• appears to be locally available
But… data is obtained by multiple instruments,
using various protocols, in differing
vocabularies, using (sometimes unstated)
assumptions, with inconsistent (or nonexistent) meta-data. It may be inconsistent,
incomplete, evolving, and distributed
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Virtual Observatories
Make data and tools quickly and easily accessible to
a wide audience.
Operationally, virtual observatories need to find the
right balance of data/model holdings, portals and
client software that a researchers can use without
effort or interference as if all the materials were
available on his/her local computer using the
user’s preferred language.
They are likely to provide controlled vocabularies
that may be used for interoperation in appropriate
domains along with database interfaces for
access and storage and “smart” tools for evolution
and maintenance.
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Virtual Solar Terrestrial Observatory
(VSTO)
• a distributed, scalable education and research
environment for searching, integrating, and analyzing
observational, experimental, and model databases.
• subject matter covers the fields of solar, solar-terrestrial
and space physics
• it provides virtual access to specific data, model, tool and
material archives containing items from a variety of
space- and ground-based instruments and experiments,
as well as individual and community modeling and
software efforts bridging research and educational use
• 3 year NSF-funded project in first year
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Inference Web and Explanation
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Inference Web
Framework for explaining reasoning tasks by storing,
exchanging, combining, annotating, filtering, segmenting,
comparing, and rendering proofs and proof fragments
provided by multiple distributed reasoners.
• OWL-based Proof Markup Language (PML) specification as
an interlingua for proof interchange
• IWExplainer for generating and presenting interactive
explanations from PML proofs providing multiple dialogues
and abstraction options
• IWBrowser for displaying (distributed) PML proofs
• IWBase distributed repository of proof-related meta-data such
as inference engines/rules/languages/sources
• Integrated with theorem provers, text analyzers, web
http://iw.stanford.edu
services, …
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SW Questions & Answers
Users can explore extracted entities and relationships,
create new hypothesis, ask questions, browse
answers and get explanations for answers.
A question
An answer
An abstracted
explanation
(this graphical interface done by Batelle supported by KSL)
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A context for
explaining
the answer
Browsing Proofs
The proof associated with an answer can be
browsed in multiple formats.
Menu to switch
between
Graphical/HTML
Proof Styles
Proof Rendered
in Graphical
Style
Provenance
Information
associated with
a selected
NodeSet
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Selected Semantic Web Tools
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Protégé
• Open source ontology
editor from Stanford
Medical Informatics
– Large user community
• Good GUI interface for
subject-matter experts
• Extra features
– SWRL support
– PROMPT versioning
• http://protege.stanford.edu
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Cerebra
• Commercial OWL DL tools
– Cerebra Construct
• Ontology engineering and external source mapping within a familiar
MS Visio framework
– Cerebra Server
• Commercial-grade inference platform, providing industry-standard
query, high-performance inference and management capabilities
with emphasis on scalability, availability, robustness and 100%
correctness. Based on initial work from University of Manchester
– CEREBRA Repository
• Collaborative object repository for metadata, vocabulary, security
and policy management
• http://www.cerebra.com
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Medius / Sandpiper
• Visual Ontology Modeler
– UML-based modeling tool
– Add-in to Rational Rose
– Produces RDF, OWL, DAML, UML, …
• Medius Knowledge Brokering Suite
• OMG Ontology Definition Metamodel
(ODM)
• http://www.sandsoft.com
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SWOOP
• Hypermedia-based
open source ontology
editor
– Includes an interface
to the Pellet OWL DL
reasoner
• http://www.mindswap.
org/2004/SWOOP/
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Pellet
• Open source Java OWL DL reasoner
– API supports
•
•
•
•
•
Species validation (OWL Lite/DL/Full)
Consistency checking
Classification
Entailment
Query
• http://www.mindswap.org/2003/pellet/
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SNOBASE
• Ontology management system from IBM
– Ontology Directory
– Query capability
– JOBC API
• http://www.alphaworks.ibm.com/tech/snobase
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Jena
• Open source API from HP Labs UK
• Most popular Java API
– Parser
– Serializer
• Extra features
–
–
–
–
Persistence (RDBMS)
Query (RDQL)
Reasoning
Rule Engine
• http://www.hpl.hp.com/semweb/
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SweetRules
• Open source rule framework
• Executes SWRL and RuleML using a variety of
rule engines
–
–
–
–
CommonRules
XSB Prolog
JESS
Jena 2
• Translates between various rule formats
• http://sweetrules.projects.semwebcentral.org
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SemWebCentral
• Open source software
development site
dedicated to the
Semantic Web
– 79+ projects
– 257+ developers
• Select projects by
workflow or other
attributes
• http://semwebcentral.org
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Other Tool Resources
• Dave Beckett’s RDF Resource Guide
– http://www.ilrt.bris.ac.uk/discovery/rdf/resources/
• Michael Denny’s Survey of Ontology Tools
– http://www.xml.com/pub/a/2004/07/14/onto.html
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More Info
Deborah McGuinness
dlm@ksl.stanford.edu
http://www.ksl.stanford.edu/people/dlm
Mike Dean
mdean@bbn.com
http://www.daml.org/people/mdean/
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Background
AT&T Bell Labs AI Principles Dept
– Description Logics, CLASSIC, explanation, ontology environments
– Semantic Search, FindUR, Collaborative Ontology Building Env
– Apps: Configurators, PROSE/Questar, Data Mining, …
Stanford Knowledge Systems, Artificial Intelligence Lab
– Ontology Evolution Environments (Diagnostics and Merging)
Chimaera
– Explanation and Trust, Inference Web
– Semantic Web Representation and Reasoning Languages, DAMLONT, DAML+OIL, OWL,
– Rules and Services: SWRL, OWL-S, Explainable SDS, KSL Wine
Agent
`McGuinness Associates
– Ontology Environments: Sandpiper, VerticalNet, Cisco…
– Knowledge Acquisition and Ontology Building – VSTO, GeON,
ImEp,…
– Applications: GM: Search, etc.; CISCO : meta data org, etc.;
– Boards: Cerebra, Sandpiper, Buildfolio, Tumri, Katalytik
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Semantic Web Layering
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From: Berners-Lee XML 2000