Presented at the NSF/NSDL workshop on scientific markup languages, 14 June 2004 http://ebiquity.umbc.edu/v2.1/resource/html/id/32/
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-- Philip Wadler,
, VLDB, Rome,
September 2001.
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-- Michael I. Jordan, paraphrased from a talk at AAAI, July 2002 by Michael Jordan (UC Berkeley)
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UMBC an Honors University in Maryland
-- Tim Berners-Lee, M.I.T.
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-- Anonymous, 2001
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Branch of metaphysics dealing with the nature of being
“Ontology” is from the Greek word ontos for being and logos for
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An ontology is a theory of what exists
It lets us experience, operate in, and talk about the world
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As Plato put it, we need to
“carve nature at its joints”
Successful communication requires a shared ontology
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Aristotle described an ontology of basic categories of predicates to describe the world (Sowa, after Brentano)
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An explicit formal specification of how to represent the objects, concepts and other domain entities and relationships among them.
Common examples: UML diagrams, Data dictionaries, DB schema, Conceptual Schemas,
API descriptions, Knowledge Bases, etc.
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Ontologies provide an abstract conceptualization of information to be represented and a vocabulary of terms to use in the representation.
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Interoperability between two systems requires them to share a common ontology.
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Databases are opaque
Typical DB schemas don’t help much
Conceptual schemas gives intended meaning of concepts used in a DB
We assume they
“bottom out” on common ontologies
“understood” by the programs involved
If not, we fail to capture the meaning
UMBC an Honors University in Maryland
139 74.50
140 77.60
… …
Table: price
*stockNo: integer; cost: float
Auto
Product
Ontology price(x, y) =>
 (x’, y’) [ auto_part(x’) Product
& part_no(x’) = x Ontology
& retail_price(x’, y’, Value-Inc)
& magnitude(y’, US_dollars) = y]
Units &
Measures
Ontology
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Systems which communicate or cooperate must share an ontology
The shared ontology can be implicit or explicit
Implicit ontology are common and typically represented only by procedures and data structures
Explicit ontologies are (ideally) given a declarative representation in a well defined knowledge representation (KR) language
Explicit ontologies enable tools and programs to
(partially) understand descriptions in ontologies if they understand the KR language.
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Three important aspects to explicit ontologies
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Conceptualization: modeling domain in terms of objects, attributes and relations.
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Vocabulary: assigning symbols or terms to refer to those objects, attributes and relations.
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Axiomitization: encoding rules and constraints to capture significant aspects of the domain model.
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Ontologies for the same domain may differ in any or all of these three levels.
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Ontologies come in all sizes but there does seem to be a split between those that like them huge and those that prefer small composable ones.
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Small ontologies include: Dublin Core, FOAF, etc.
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Some large, general ontologies are freely available:
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Cyc - Original general purpose ontology
OntoSem – UMBC’s lexical KR system and ontology
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WordNet - a large, on-line lexical reference system
World Fact Book -- 5Meg of KIF sentences of geo-political facts
UMLS - NLM’s Unified Medical Language System
SUMO – Standard Upper Merged Ontology
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The
Web
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Today there are a few languages for the semantic web, with more to come (probably)
The RDF family:
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RDF – Resource Description Framework http://www.w3.org/RDF/
DAML+OIL – Darpa Agent Markup
Language http://www.daml.org/
Deprecated
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OWL – Ontology Web Language http://www.w3.org/2001/sw/
Topic maps (http://topicmaps.org/) are another species, not based on RDF
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Graph
XML Encoding
<rdf:RDF ……..>
<….>
<….>
</rdf:RDF>
Good for
Machine
Processing
RDF
Data Model
Good For
Human
Viewing
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Triples stmt(docInst, rdf_type, Document) stmt(personInst, rdf_type, Person) stmt(inroomInst, rdf_type, InRoom) stmt(personInst, holding, docInst) stmt(inroomInst, person, personInst)
Good For
Reasoning
RDF is a simple language for building graph based representations
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An RDF document is an unordered collection of statements, each with a subject, predicate and
object (aka triples)
A triple can be thought of as a labelled arc in a graph
Statements describe properties of web resources
A resource is any object that can be pointed to by a
URI:
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 a document, a picture, a paragraph on the Web, …
E.g., http://umbc.edu/~finin/cv.html
a book in the library, a real person (?) isbn://5031-4444-3333
…
Properties themselves are also resources (URIs)
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URI = Uniform Resource Identifier
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"The generic set of all names/addresses that are short strings that refer to resources"
URLs (Uniform Resource Locators) are a subset of URIs, used for resources that can be accessed on the web
URIs look like “normal” URLs, often with fragment identifiers to point to a document part:
 http://foo.com/bar/mumble.html#pitch
URIs are unambiguous, unlike natural language terms
 the web provides a global namespace
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Assumption: references to the same URI are to the
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http://umbc.edu/
~finin/talks/idm02/ dc:Title dc:Creator
“Intelligent Information Systems on the Web and in the Aether” http://umbc.edu/
UMBC an Honors University in Maryland bib:Aff bib:name
“Tim Finin” bib:email
“finin@umbc.edu”
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<rdf:RDF xmlns:rdf=" http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc ="http://purl.org/dc/elements/1.1/" xmlns:bib=" http://daml.umbc.edu/ontologies/bib/">
<description about="http://umbc.edu/~finin/talks/idm02/">
<dc:title> Intelligent Information Systems on the Web and in the
Aether </dc:Title>
<dc:creator>
<description>
<bib:Name>Tim Finin</bib:Name>
<bib:Email>finin@umbc.edu</bib:Email>
<bib:Aff resource="http://umbc.edu/" />
</description>
</dc:Creator>
</description>
</rdf:RDF>
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RDF can be encoded as a set of triples.
<
> <
> <
> .
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<http://umbc.edu/~finin/talks/idm02/>
<http://purl.org/dc/elements/1.1/Title>
"Intelligent Information Systems on the Web and in the Aether" .
_:j10949 <http://daml.umbc.edu/ontologies/bib/Name> "Tim Finin" .
_:j10949 <http://daml.umbc.edu/ontologies/bib/Email> "finin@umbc.edu" .
_:j10949 <http://daml.umbc.edu/ontologies/bib/Aff> <http://umbc.edu/> .
_:j10949 http://www.w3.org/1999/02/22-rdf-syntax-ns#type <Description> .
<http://umbc.edu/~finin/talks/idm02/>
<http://purl.org/dc/elements/1.1/Creator> _:j10949 .
<http://umbc.edu/~finin/talks/idm02/> http://www.w3.org/1999/02/22-rdfsyntax-ns#type <Description> .
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Note the gensym for the “blank node” _:j10949
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RDF triples have one of two forms:
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<URI> <URI> <URI>
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<URI> <URI> <quoted string>
Triples are also easily mapped into logic
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<subject> <predicate> <object> becoming:
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<predicate>(<subject>,<object>)
With type(<S>,<O>) becoming <O>(<S>)
Example:
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 subclass(man,person) sex(man,male) domain(sex,animal) man(adam) age(adam,100)
; Note: we’re not
; showing the actual
; URIs for clarity
Triples are easily stored and queried in a DBMS
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Flat nature of a triple a good match for relational DBs
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FOAF (Friend of a Friend) is a simple ontology to describe people and their social networks.
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See the foaf project page: http://www.foaf-project.org/
We recently crawled the web and discovered over 1,000,000 valid
RDF FOAF files.
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Most of these are from the http://liveJournal.com/ blogging system which encodes basic user info in foaf
See http://apple.cs.umbc.edu/semdis/wob/foaf/
<foaf:Person>
<foaf:name>Tim Finin</foaf:name>
<foaf:mbox_sha1sum>2410…37262c252e</foaf:mbox_sha1sum>
<foaf:homepage rdf:resource="http://umbc.edu/~finin/" />
<foaf:img rdf:resource="http://umbc.edu/~finin/images/passport.gif" />
</foaf:Person>
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FOAF vocabulary provides 50+ basic terms for making simple claims about people
FOAF files can use other RDF terms too: RSS,
MusicBrainz, Dublin Core, Wordnet, Creative Commons, blood types, starsigns, …
RDF guarantees freedom of independent extension
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OWL provides fancier data-merging facilities
Result: Freedom to say what you like, using any RDF markup you want, and have RDF crawlers merge your
FOAF documents with other’s and know when you’re talking about the same entities.
After Dan Brickley, danbri@w3.org
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Consequence:
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We must plan for lies, mischief, mistakes, stale data, slander
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Dataset is out of control, distributed, dynamic
Importance of knowing who-said-what
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Anyone can describe anyone
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We must record data provenance
Modeling and reasoning about trust is critical
Legal, privacy and etiquette issues emerge
Welcome to the real world
After Dan Brickley, danbri@w3.org
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RDF has a solid specification
RDF is being used in a number of web standards
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CC/PP (Composite Capabilities/Preference Profiles)
P3P (Platform for Privacy Preferences Project)
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RSS (RDF Site Summary)
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RDF Calendar (~ iCalendar in RDF)
And in other systems
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Netscape’s Mozilla web browser
Open directory (http://dmoz.org/)
Adobe products via XMP (eXtensible Metadata Platform)
Web communities: LiveJournal , Ecademy , and Cocolog .
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We’ve found over 1.6M RDF documents on the web.
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RDF Schema adds taxonomies for classes & properties
 subClass and subProperty and some metadata.
 domain and range constraints on properties
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Several widely used
KB tools can import and export in RDFS
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Stanford Protégé KB editor
• Java, open sourced
• Extensible, lots of plug-ins
• Provides reasoning & server capabilities
• Imports and exports RDF and OWL
• See http://protege.stanford.edu/
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An RDF ontology plus some RDF statements may imply additional RDF statements.
This is not true of XML.
Example: domain(parent,person) range(parent,person) subproperty(mother,parent) range(mother,woman) mother(eve,cain)
New and
Improved!
100% Better than XML!!
Implies: subclass(woman,person) parent(eve,cain) person(eve) person(cain) woman(eve)
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This is part of the data model and not of the accessing/processing code
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Q: For a specific application, should I use XML or RDF?
A: It depends…
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XML's model is
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 a tree, i.e., a strong hierarchy applications may rely on hierarchy position relatively simple syntax and structure not easy to combine trees
RDF's model is
 a loose collections of relations
 applications may do “database”-like search
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 not easy to recover hierarchy easy to combine relations in one big collection great for the integration of heterogeneous information
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RDFS too weak to describe resources in sufficient detail, e.g.:
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No localised range and domain constraints
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Can’t say that the range of hasChild is person when applied to persons and elephant when applied to elephants
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No existence/cardinality constraints
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Can’t say that all instances of person have a mother that is also a person, or that people have exactly 2 parents
No transitive, inverse or symmetrical properties
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Can’t say that isPartOf is a transitive property, that hasPart is the inverse of isPartOf or that touches is symmetrical
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OWL is layered atop RDFS to provide these and other features.
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DAML+OIL begat OWL.
OWL released as W3C recommendation 2/10/04
See http://www.w3.org/2001/sw/WebOnt/ for
OWL overview, guide, specification, test cases, etc.
Three layers of OWL are defined of decreasing levels of complexity and expressiveness
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OWL Full is the whole thing
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OWL DL (Description Logic) introduces restrictions
OWL Lite is an entry level language intended to be easy to understand and implement
OWL
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
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An OWL ontology is a set of RDF statements
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OWL defines semantics for certain statements
Does NOT restrict what can be said -- documents can include arbitrary RDF
But no OWL semantics for non-OWL statements
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Adds capabilities common to description logics:
 cardinality constraints, defined classes (=> classification), equivalence, local restrictions, disjoint classes, etc.
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More support for ontologies
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Ontology imports ontology, versioning, …
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But not (yet) variables, quantification, & rules
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A complete OWL reasoning is significantly more complex than a complete RDFS reasoner.
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Validators to check syntax, identify language & level, ...
Visualizer to draw graphs, UML diagrams, etc.
APIs to parse XML or N3, import ontologies, draw inferences, materialize model as set of triples, …
Editors that create, modify, browse ontologies & KBs
Reasoners for heavy duty reasoners for OWL
Markup tools help authors add SW annotations to other documents
Query languages designed for SW languages
Database systems for storing SW content and answering queries about it
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E.g., RSS, CCPP, P3P, FOAF tools
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We’ll describe some research at UMBC
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SWAD-Europe survey:
 http://www.w3.org/2003/11/SWApplSurvey
 lists more than 50 applications in 12 categories…
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Rich Site Summary or
RDF Site Summary
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A lightweight multipurpose extensible metadata description & syndication format for the web
 news & other headline syndication
 weblog syndication
 propagation of software update lists.
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<channelrdf:about="http://agents.umbc.edu/awchannel
.rdf">
<title>UMBC AgentWeb</title>
<link>http://agents.umbc.edu/</link>
<description>UMBC AgentWeb...</description>
<language>en-us</language>
<copyright>copyright...</copyright>
<managingEditor>Tim...</managingEditor>
<pubDate> Mon ... </pubDate>
<lastBuildDate> Mon ... </lastBuildDate>
<webMaster>finin@umbc.edu</webmaster>
<rating>(PICS-1.1 ... </rating>
<image rdf:resource="http:..." />
<items><rdf:Seq>...</rdf:Seq></items>
</channel>
<image>
<title>UMBC AgentWeb</title>
<url>http://agents...</url>
<link>http://agents.umbc.edu/</link>
<width>46</width> <height>66</height>
</image>
<item rdf:about="http:...">
<title>UDDIe</title>
<link>http://...</link>
<description>the ...</item>
</rdf:RDF>
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•
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ITTALKS is a database driven web site of IT related talks at UMBC and other institutions. The database contains information on
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Seminar events
–
–
People (speakers, hosts, users, …)
Places (rooms, institutions, …)
Web pages with DAML markup are generated
The DAML markup supports agent-based services relating to these talks.
Users get talk announcements based on the interests, locations and schedules.
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UMBC an Honors University in Maryland human view
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UMBC an Honors University in Maryland machine view
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Our research group’s web site generate both
HTML and OWL.
HOW? This is relatively easy since the content is in a database.
PHP is sufficient for the job.
HTML pages have links to corresponding OWL
WHY? This exposes the information to programs and agents – no more web scraping.
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Motivation
Market dynamics
Auction theory (TAC)
Semantic web
Agent collaboration
(FIPA & Agentcities)
Travel Agent Game in Agentcities
Features
Open Market Framework
Auction Services
OWL message content
OWL Ontologies
Global Agent Community
Technologies
FIPA (JADE, April Agent Platform)
Semantic Web (RDF, OWL)
Web (SOAP,WSDL,DAML-S)
Internet (Java Web Start )
Ontologies http://taga.umbc.edu/ontologies/ travel.owl
– travel concepts fipaowl.owl
– FIPA content lang.
auction.owl
– auction services tagaql.owl
– query language
Report Contract Report Direct Buy Transactions
Market Oversight
Agent
Report Travel Package
Report Auction Transactions
Bulletin Board
Agent
Auction Service
Agent
Customer
Agent
Proposal
Direct Buy
Travel Agents Web Service
Agents
FIPA platform infrastructure services, including directory facilitators enhanced to use DAML-S for service discovery http://taga.umbc.edu/
UMBC
Acknowledgements: DARPA contract F30602-00-2-0591 and Fujitsu Laboratories of America.
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Developing useful upper ontologies
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For time, space, web services, …
Some standard problems
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Ontology alignment and mapping, learning ontologies, automating markup, …
Extending OWL
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Adding rules, uncertainty, query languages, …
Integrating information
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Representing and using provenance, trust, …
Integrations with
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Agents, web services, information retrieval, …
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Efficient tools
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Good applications
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CGI scripts
SWOs
Video files
HTML documents
Audio files SWIs
Web services
APIs
Images Agent service s
Swoogle is a crawler based search & retrieval system for semantic web documents (SWDs) in RDF, Owl and DAML. It discovers SWDs and computes their metadata and relations, and stores them in an IR system.
Web interface
Apache/
Tomcat php, myAdmin
Focused
Crawler
SWD = SWO + SWI
The web, like Gaul, is divided into three parts: the regular web (e.g. HTML), Seman- tic
Web Ontologies (SWOs), and Semantic Web
Instance files (SWIs)
SWD Properties
Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form
(RSS, FOAF, P3P, …); rank; weight; annotations; …
SWD Relations
Binary: R(D1,D2)
•
IM: D1 owl:imports D2
•
IMstar: transitive closure of IM
•
EX: D1 extends D2 by defining classes or properties subsumed by D2’s
• PV: owl:priorVersion & subproperties
•
TM: D1 uses terms from D2
•
IN: D1 uses individual defined in D2
•
MAP:
D1 maps some of its terms to D2’s
•
SIM: D1 & D2 are similar
• EQ: D1 & D2 are identical
•
EQV: D1 & D2 have the same triples
Ternary: R(D1,D2,D3)
•
MP3: D1 maps a term from D2 to D3 using owl:sameClass, etc.
UMBC an Honors University in Maryland
Ontology
Analyzer
Jena mySQL
DB
IR engine
SWD crawler
Jena
We b
Agents
Agents cached files
SIRE
Ontology discovery
Ontology discovery
Swoogle uses two kinds of crawlers to discover semantic web documents and several analysis agents to compute metadata and relations among documents and ontologies. Metadata is stored in a relational DBMS.
SWD Rank
A SWD’s rank is a function of its type
(SWO/SWI) and the rank and types of the documents to which it’s related.
Swoogle v1 has ~12K SWDs & 100K relations. v2 will also catalog classes and properties and their metadata and have >1.6M SWDs.
SWD IR Engine
Swoogle puts documents into a character ngram based IR engine to compute document similarity and do retrieval from queries
Contributors include Tim Finin, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, Pavan Reddivari, Vishal Doshi,
Rong Pan, Li Ding, and Drew Ogle. Partial research support was provided by DARPA contract F30602-00-0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITR-IDM-0219649. 20 May 2004.
59

Knowledge Discovery in the Semantic Web
NSF award ITR-IIS-0325464
U. Georgia, Sheth (PI), Arpinar (CO-PI), Kochut, Miller
NSF award ITR-IIS-0325172
UMBC, Joshi (PI), Yesha (CO-PI), Finin
Objective
Design, prototype and evaluate a system supporting the discovery, indexing and querying of complex semantic relationships in the Semantic Web. The system maintains and utilizes trust and provenance information to enhance the relationship discovery.
AssociationConnective connective xsd:real [0,1] confidence
Association
DocumentRelation foaf:Document
Justification
Belief
Approach
Knowledge representation systems reason over semantic web content discovered on the web which is reduced to triples that can be efficiently stored and processed in relational databases.
Trust models and heuristics guide the formation of conclusions
Trust
A “web of belief” model and associated ontology is used to represent, integrate, and evaluate conclusions drawn from the large volume of heterogeneous assertions found in the data.
Broader impacts
Techniques and prototypes developed can be applied to a range of problems, including discovering new connections and relations in scientific information and homeland security.
Reference foaf:Agent source selects FOAF Network island
Kagal source
L. Ding
P. Kolari
A. Joshi knows
T. Finin
H. Chen knows
F. Perich hub knows
Y. Yesha
J. Golbeck
J. Hendler sink
Golbeck’s
Trust Network mapTo
Ding
Kagal
Chen
Finin
28
A. Joshi
Y. Peng
1
6
1
M. P. Singh
5
A. Sheth
An experimental algorithm has been developed to integrate and rank discovered relationships.
SWETO is large ontology covering several test-bed domains. It is pop-ulated with 800K instances and 1.M relations extracted from heterogeneous Web sources. SWETO was developed using Semagix Freedom system.
UMBC
Perich
DBLP Network
60 http://semdis.umbc.edu/
June 2004

Invasive species do more economic damage to the U.S. every year that all other natural disasters combined. Above: plants, animals, and a virus.
Semantic Prototypes in Research Ecoinfomatics
Spire is a distributed, interdisciplinary research project exploring how semantic web technology supports information discovery, integration, and sharing in scientific communities. We are building prototype tools and applications for inclusion in the
National Biological Information Infrastructure (NBII), with a focus on the early detection and warning of invasive species.
Approach
We are building prototype tools and applications that demonstrate how semantic web technology supports information discovery, integration and sharing in scientific communities. The National Biological Information Infrastructure
(NBII) and Invasive Species Forecasting System (ISFS) provide requirements and serve as testbeds for our prototypes.
Significant Results
•
•
•
•
•
•
•
•
SWOOGLE - a search engine for the semantic web.
MoaM (Meal of a Meal) - Given a species list, infer a food web.
Photostuff - annotate regions of a picture with OWL.
SWOOP - the first ontology editor written specifically for OWL.
Ontologies for ecological interaction, and observation data.
Food web visualization and analysis tools that are driven by OWL ontologies and instance data.
CRISIS CAT - an RDF based catalog of Invasive Species resources in California.
Coordination with USGS, NASA, EPA, GBIF, and the
Intergovernmental, Interagency Cooperation on Ecoinformatics.
The RMBL team expresses food webs in OWL using an ontology for ecological interaction they have constructed in coordination with other ecologists. The OWL model drives the simulation and visualization.
The NBII California Information Node (CAIN), maintained by
UC Davis, is a jumping off point to broader NBII deployment.
UMBC
AN HONORS UNIVERSITY IN MARYLAND
Meal of a Meal (after Friend of a Friend). We know
Fish 1 eats Plant 1. We then infer that Fish 1 may also eat the taxonomic siblings of Plant 1: Plants 2 and 3. Similarly, we infer that the taxonomic siblings of Fish 1 - Fishes 2 and 3 - may eat Plant 1.
An ontology (found via Swoogle) is loaded into Photostuff to mark up regions of a field photograph.
Swoogle is a crawler based search and retrieval system for semantic web documents (SWDs) in RDF and OWL. It discovers SWDs and computes their metadata and relations, and stores them in an IR system. Users can search for ontologies or instance data, and hits are ranked according to our Ontology
Rank algorithm.
•
•
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Broader Impacts
Enable knowledge from one community to be effectively used by another.
Harness the power of the citizen scientist. (The majority of invasives are discovered by amateurs.)
Integrate research and education in the classroom.
Coming Soon
• ELVIS – an end to end application that starts with a location and produces a model of its food web.
• The Pond Project - a junior high school classroom activity to monitor the health of local ecosystems.
• Enhanced tools.
Spatial distribution of exotic plants at the Cerro Grande fire site. The statistical techniques used to generate these maps do not take trophic data as input. Yet.
Research Team
UMBC ebiquity (Finin)
UMBC GEST Center (Sachs)
UMD MINDSWAP (Hendler)
UC Davis ICE (Quinn)
RMBL PEaCE (Martinez)
NASA GSFC (Schnase)

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RDF and OWL are based in a knowledge/logic model
As a computer scientist, I have to admit that this is only appropriate for representing and reasoning about *some* information
So, agents should probably live in a world populated with objects, procedures, DBs, XML, topic maps, RDF, etc.
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Not everyone likes using RDF as the foundation for a KB compatible markup language
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Graph-based representations have well known problems
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The semantics of RDF also constrains higher layers like OWL
Basing a language like OWL or a rule language like RuleML directly on XML is an alternative
IMHO, the train has left the station, it’s a pretty good train, and it’s got momentum. We should see if it makes it up the hill before starting on a new one
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Rethinking the agent communication
A possible new paradigm?
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Agents “publish” beliefs, requests, and other
“speech acts” on web pages.
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Brokers “search” for and “index” published content
Agents “discover” what peers have published on the web and browse for more details
Agents “speak for” content on web pages by
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Answering queries about them
Accepting comments and assertions about them
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This semantic web emphasizes ontologies – their development, use, mediation, evolution, etc.
It will take time to deliver on the intelligent agent paradigm, either on the Internet or in a pervasive computing environment.
The development of complex systems is basically an evolutionary process.
Random search carried out by tens of thousands of researchers, developers and graduate students.
As long as some incremental benefits accrue, the process is worth it
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Start with the simple and move toward the complex
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E.g., from vocabularies to FOL theories
Allow many ontologies to bloom
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Let natural evolutionary processes select the most useful as common consensus ontologies.
Support diversity in ontologies
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Monocultures are unstable
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There should be no THE ONTOLOGY FOR X.
The evolution of powerful, machine readable ontologies will take many years, maybe generations
Incremental benefits will more than pay for effort
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