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Semantic Web Technologies: A Tutorial. Li Ding University of Maryland Baltimore County Joint work with Deborah McGuinness, Tim Finin and Anupam Joshi Presented at Kodak Research Laboratories , Rochester, New York 18 July 2006. The Web has made people smarter. craigslist . Surfing. WWW.

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semantic web technologies a tutorial

Semantic Web Technologies:A Tutorial

Li Ding

University of Maryland Baltimore County

Joint work with Deborah McGuinness, Tim Finin and Anupam Joshi

Presented at Kodak Research Laboratories, Rochester, New York

18 July 2006

the web has made people smarter
The Web has made people smarter

craigslist

Surfing

WWW

Search

bag-of-words

tagging

del.icio.us

motivation machine friendly data
Motivation: machine-friendly data
  • Natural Language
  • XML – represent structures
  • Semantic Web - represent more semantics
    • represent structures
    • enable common vocabulary
    • associate symbols with logic interpretation for inference

Li Ding is a person

LiDingisasaon

as seen by a machine

as seen by a person

<on>LiDing</on>

<person>Li Ding</person>

as seen by a person

as seen by a machine

semantic web layers
Semantic Web Layers

Semantic

Aspect

Web

Aspect

HTTP

"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“ – Berners-Lee, Hendler & Lassila, Scientific American, 2001

Image source: http://en.wikipedia.org/wiki/Image:W3c_semantic_web_stack.jpg

the semantic web is simple
The Semantic Web is simple
  • Each URI denotes a concept
  • URIs are connected by triples
  • Machines read data as directed RDF graph

Don't say "colour" say <http://example.com/2002/std6#col>

RDF (Resource Description Framework)

Relational database

Source: Tim Berners-Lee, Putting the Web back into Semantic Web, ISWC2005 Keynote

example rdf graph and syntax
Example: RDF graph and syntax

http://xmlns.com/foaf/0.1/name

  • RDF Graph
  • URI, Literal, BNode
  • Triple

Li Ding

t1

http://www.w3.org/1999/02/22-rdf-syntax-ns#type

t2

http://xmlns.com/foaf/0.1/Person

The entire graph means: there exist a person whose name is “Li Ding”.

<?xml version="1.0" encoding="utf-8"?>

<rdf:RDF xmlns:foaf=http://xmlns.com/foaf/0.1/

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#“>

<foaf:Person>

<foaf:name>Li Ding</foaf:name>

</foaf:Person>

</rdf:RDF>

  • XML
  • unicode
  • Namespace
  • URI as tag

Data encoded in RDF/XML syntax

Alternative RDF syntax languages: N3(notation 3), N-Triples, Turtle

example surfing rdf graphs
Example: Surfing RDF graphs

G1: http://cs.umbc.edu/~dingli1/foaf.rdf

Surf to definition

http://cs.umbc.edu/~dingli1/foaf.rdf#dingli

foaf:name

G3: http://xmlns.com/foaf/1.0/

rdf:type

foaf:knows

Li Ding

foaf:Person

wordNet:Agent

rdf:type

foaf:mbox

mailto:finin@umbc.edu

rdfs:subClassOf

rdfs:seeAlso

foaf:Person

http://cs.umbc.edu/~finin/foaf.rdf

rdf:type

rdfs:Class

Surf to another instance

rdfs:domain

foaf:mbox

G2: http://cs.umbc.edu/~finin/foaf.rdf

foaf:mbox

rdf:type

mailto:finin@umbc.edu

rdf:Property

foaf:firstName

Tim

foaf:surname

rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#

rdfs: http://www.w3.org/2000/01/rdf-schema#

foaf: http://xmlns.com/foaf/1.0/

Finin

example serving human machine
Example: Serving human & machine

The Original RDF/XML for machines

The HTML is generated by applying XSLT on RDF/XML

ontology spectrum
Ontology Spectrum

Thesauri

“narrower

term”

relation

space of interest

Disjointness, Inverse,part of…

Frames

(properties)

Formal

is-a

Catalog/ID

CYC

DB Schema

UMLS

RDF

RDFS

DAML

Wordnet

OO

OWL

IEEE SUO

Formal

instance

General

Logical

constraints

Value Restriction

Terms/

glossary

Informal

is-a

ExpressiveOntologies

SimpleTaxonomies

Source: Originally by Deborah L. McGuinness (KSL, Stanford), modified by Tim Finin

ontology languages rdfs and owl
Ontology Languages: RDFS and OWL
  • RDFS
    • Set theory – rdfs:Class
    • Relation – rdf:Property, rdfs:domain, rdfs:range
    • Hierarchy – rdfs:subClassOf, rdfs:subPropertyOf
    • Built-in Datatype – xsd:string, xsd:dataTime
  • OWL
    • Description Logic
      • Class, Thing, Nothing
      • DatatypeProperty, ObjectProperty, AnnotationProperty,…
    • Class axioms
      • oneOf, disjointWith, unionOf, complementOf, intersectionOf …
      • Restriction, onProperty, cardinality, hasValue…
    • Property axioms
      • inverseOf , TransitiveProperty , SymmetricProperty
      • FunctionalProperty, InverseFunctionalProperty
    • Equality– equivalentClass , sameAs , differentFrom…
    • Ontology annotation – Ontology, imports, versionInfo
example inference using ontologies
Example: Inference using ontologies
  • Ontology Languages (RDFS, OWL) has formal foundations that allow us to infer additional (implicit) statements
    • RDFS provides basic ones, e.g. sub-class, sub-property, domain
    • OWL adds many more axioms, e.g. inverse-property, equality,
  • SWRL (Semantic Web Rule Language) enables a general purposed solution
    • Supports rule representation
    • But also requires inference support beyond RDFS and OWL

hasbrother rdfs:subPropertyOf hasSibling

hasChild owl:inverseOf hasParent

hasSibling

hasParent

#Joe

#Louise

#Deborah

hasBrother

hasChild

hasUncle

SWRL: (x hasParent y) (y hasBrother z) => (x hasUncle z)

Source: Semantic Web tutorial (AAAI 2005) by Deborah L. McGuinness

more languages and more ontologies
More languages and more ontologies
  • Languages (require special inference engine)
    • [Trust/Uncertainty] BayesOWL
    • [Proof] PML (Proof Markup Language)
    • [Query/Data Access] SPARQL Query Language for RDF
    • [Rule] SWRL( Semantic Web Rule Language)
    • [Policy] REI: A Policy Specification Language
    • [Service] OWL-S by DAML (1.2 preview available)
    • [Service] SAWSDL (Semantic Annotations for WSDL)
    • [Thesauri] SKOS (Simple Knowledge Organization System)
  • Ontologies (only need RDFS and/or OWL inference)
    • Upper ontologies - OpenCyc, WordNet, OntoSem, SUO
    • Specialized common ontologies - FOAF, Dublin Core, RSS
    • Domain ontologies – bibtex, biology, and many…

Li Ding, Pranam Kolari, Zhongli Ding, and Sasikanth Avancha, “Using Ontologies in the Semantic Web: A Survey”, in Ontologies in the Context of Information Systems (book chapter), 2005. http://ebiquity.umbc.edu/paper/html/id/257/

semantic web tools
Semantic Web Tools
  • Pellet (DL)
  • Racer (DL)
  • FACT++ (DL)
  • Jena
  • JTP
  • F-OWL
  • Euler
  • CWM

Editor

Online Registry

  • Protégé
  • Swoop
  • DAML Ontology Library
  • Schema Web

Reasoner

  • Jena (SPARQL)
  • KAON
  • Kowari
  • Seasam
  • OWLIM
  • 3store
  • Instance store
  • Redland
  • Tap
  • RDF store
  • Yars
  • IBM IODT
  • RDFLib
  • RDF gateway
  • allegro
  • Oracle 10

create

Search Engine

publish

inference

  • Swoogle
  • Semantic Web Search

Managing

Ontologies

instance

Triple store

browse

Browser

update

  • Tabulator
  • IsaViz
  • Piggybank
  • Arago
  • Horus
  • Mspace
  • Magpie

extend

integrate

  • ONION
  • PROMPT
  • OntoMapper
  • Glue
  • OntoMerge
  • Ontomorph

Mapping Tools

source1: http://ebiquity.umbc.edu/paper/html/id/257/Using-Ontologies-in-the-Semantic-Web-A-Survey

source2: http://www.wiwiss.fu-berlin.de/suhl/bizer/toolkits/

semantic web data sources
Semantic Web data sources
  • Text editor: I write RDF/XML manually.
  • Semantic Web Editors: Protégé, Swoop
  • Information Extraction (consumer side)
    • NLP (hard), e.g. SemNews
    • heuristic scrapping (regular expr.), e.g. Semagix Freedom
  • Wrapped database content (publisher side)
    • blog, social network websites, e.g. livejournal.com
    • academic interests: http://www.mindswap.org/, http://ebiquity.umbc.edu
  • Generated by software
    • creative commons license embedded in HTML
    • embedded metadata JPEG, PDF (XMP)
    • agent communication message
the scale of the semantic web
The Scale of the Semantic Web

Statistics based Semantic Web data indexed by Swoogle

Estimated number of documents based on Google query

where the data from
Where the data from
  • “com” has contributed the largest portion of websites (71%) and pure SWDs (39%) because industry has adopted virtual hosting technology as well as ontologies such as RSS and FOAF
  • most SWOs are from “org” (46%, e.g. www.w3.org) and “edu” (14%, e.g., spire.umbc.edu) because of the deep interests in developing ontologies from academia and non-profit organizations.

SWDs: Semantic Web documents; SWOs: semantic web ontologies; pure SWD: not embeded

note: Statistics of top level domain is also used in characterizing the Web (Henziger and Lawrence 2004)

source websites of swd
Source websites of SWD

Jan 2005- Aug 2005

Jan 2005- Mar 2006

  • Invariant found!
    • The number of websites hosting more than m SWDs follows power law distribution
    • Similar to the Web
  • Head: virtual hosting
  • Tail: crawling strategy
size of swd
Size of SWD
  • Embedded SWDs are small
    • 69% have 3 triples
    • 96% have <10 triples;
  • Pure SWDs
    • 60% have 5 to 1000 triples.
    • Special size of RSS 130
      • 17 triples for channel
      • 7 triples for each of the 15 items
  • SWOs
    • Biased by PML,
    • Small ones from RDF test
    • Largest is 1M

Number of SWDs

Number of SWOs

# of triples

age of swd
Age of SWD
  • Measured by the last-modified time of SWD
    • PSWD: Exponential distribution
    • SWO: flat tail -- ontology development interests decrease?
how semantic web terms are used
How Semantic Web Terms are used?
  • All usage distributions follow Power distribution
  • Few SWTs been well populated
    • 371 has >100 class-instance
    • 1208 has>100 property-instances
swoogle rank citation based
Swoogle Rank (citation based)

http://www.w3.org/2000/01/rdf-schema

indegree=432,984,mean(inflow)=0.039

http://www.w3.org/1999/02/22-rdf-syntax-ns

0.51

1

indegree=1,077,768,mean(inflow)=0.100

0.11

0.10

0.25

2

0.30

0.35

5

0.11

http://purl.org/rss/1.0

http://www.w3.org/2002/07/owl

0.03

indegree=86,959,mean(inflow)=0.069

indegree=270,178,mean(inflow)=0.168

0.18

0.10

0.20

0.16

6

8

0.12

http://web.resource.org/cc

0.43

0.17

indegree=57,066,mean(inflow)=0.195

0.21

0.27

0.27

9

0.07

0.10

4

http://www.w3.org/2001/vcard-rdf/3.0

0.10

0.07

indegree=155,949,mean(inflow)=0.036

0.25

0.12

0.11

0.06

0.23

0.12

0.16

0.05

http://purl.org/dc/elements/1.1

10

0.03

indegree=861,416,mean(inflow)=0.096

7

0.20

http://www.hackcraft.net/bookrdf/vocab/0_1/

http://purl.org/dc/terms

0.08

indegree=16,380,mean(inflow)=0.167

indegree=54,909,mean(inflow)=0.042

0.17

3

http://xmlns.com/foaf/0.1/index.rdf

0.29

indegree=512,790,mean(inflow)=0.217

Computed using Swoogle metadata by May 2006

taga travel agent game in agentcities

Report Direct Buy Transactions

Report Contract

Report Auction Transactions

Market Oversight

Agent

Request

CFP

Report Travel Package

Bid

Bid

Bulletin Board

Agent

Auction Service

Agent

Customer

Agent

Proposal

Direct Buy

Travel Agents

Web Service

Agents

TAGA: Travel Agent Game in Agentcities

Motivation

  • Market dynamics
  • Auction theory (TAC)
  • Semantic web
  • Agent collaboration (FIPA & 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 )

Ontologieshttp://taga.umbc.edu/ontologies/

  • travel.owl – travel concepts
  • fipaowl.owl – FIPA content lang.
  • auction.owl – auction services
  • tagaql.owl – query language

Owl for representation and reasoning

Owl for protocol description

Owl as a content language

Owl for service descriptions

FIPA platform infrastructure services, including directory facilitators enhanced to use OWL-S for service discovery

http://taga.umbc.edu (offline now)

semantic content publishing
Semantic Content Publishing

http://ebiquity.umbc.edu/person/html/Li/Ding/

  • data stored in database
  • PHP generates both HTML and OWL
  • HTML pages link to corresponding OWL
  • no more web scraping

http://ebiquity.umbc.edu/person/foaf/Li/Ding/foaf.rdf

FOAF

PHP

PHP

Mysql database

http://ebiquity.umbc.edu/ -- ebiquity group website

rei policy language
Rei Policy Language
  • Rei is a declarative policy language for describing policies over actions
    • Reasons over domain dependent information
  • Currently represented in OWL + logical variables
  • Based on deontic concepts
    • Permission, Prohibition, Obligation, Dispensation
  • Models speech acts
    • Delegation, Revocation, Request, Cancel
  • Meta policies
    • Priority, modality preference
  • Policy engineering tools
    • Reasoner, IDE for Rei policies in Eclipse

http://rei.umbc.edu/

example enforcing privacy policy
Example: enforcing privacy policy
  • The speaker doesn’t want others to know the specific room that he’s in, but is willing for others to know he’s on campus
  • He defines the following privacy policy
    • Share my location with a granularity >= “State”
  • The broker
    • isLocated(US) => Yes!
    • isLocated(Maryland) => Yes!
    • isLocated(UMBC) => Uncertain..
    • isLocated(ITE-RM210) => Uncertain..
cobra context broker architecture
Cobra: Context Broker Architecture
  • Ontology
  • Agents
  • Service
  • Inference
  • Policy

http://cobra.umbc.edu/

web scale semantic web data access
Web-scale semantic web data access

data access service

the Web

agent

Index RDF data

ask (“person”)

Search vocabulary

Search URIrefs

in SW vocabulary

inform (“foaf:Person”)

Compose query

ask (“?x rdf:type foaf:Person”)

Search URLs

in SWD index

Populate

RDF database

inform (doc URLs)

Fetch docs

Query local

RDF database

swoogle semantic web search engine
Swoogle Semantic Web Search Engine
  • Harvesting Semantic Web data from the Web
  • Provide search/navigation services for machines (via REST+ RDF/XML)
    • Digest doc, term, namespace
    • Links
  • Also serves human users
  • Status
    • Running since summer 2004
    • 1.6M RDF documents, 300M RDF triples, 10K ontologies

http://swoogle.umbc.edu/

ontology dictionary
Ontology Dictionary
  • From web of document to web of data
  • Aggregate from multiple sources
  • Inductively learned definition

Onto 1

Onto 2

rdf:type

owl:Class

foaf:name

rdfs:domain

foaf:Person

foaf:Person

foaf:Agent

rdfs:subClassOf

foaf:name

rdfs:domain

rdf:type

owl:Class

wob:hasInstanceDomain

foaf:Person

wob:hasInstanceDomain

foaf:Agent

dc:title

rdfs:subClassOf

SWD3

foaf:name

Tim Finin

rdf:type

foaf:Person

dc:title

Dr.

http://swoogle.umbc.edu/2005/modules.php?name=Ontology_Dictionary

semantic web challenges winners
Semantic Web Challenges - Winners

2003

2004

Flink itself is also likely to be unique as a crossover between a social experiment and a semantic application.

CS AKTive Space (CAS) is an integrated Semantic Web application which provides a way to explore the UK Computer Science Research domain across multiple dimensions for multiple stakeholders, from funding agencies to individual researchers.

2005

CONFOTO is a browsing and annotation service for conference photos.

http://challenge.semanticweb.org/

triple shop sparql dataset finder
Triple Shop: SPARQL dataset finder

Who knows Anupam Joshi?

Show me their names, email address and pictures

1. Compose a SPARQL query

without FROM clause

2. Parse SPARQL query, search

Swoogle for related URLs,

and compose a dataset

3. Run SPARQL query on dataset

http://sparql.cs.umbc.edu/tripleshop2/

integrating social networks
Integrating Social Networks

FOAF Network

Reputation Systems

data

  • FOAF
    • knows RDF
    • RDF/XML
  • DBLP
    • Coauthor Database
    • HTML
  • Trust
    • Reputation
    • Trust network

Computation

  • Entity mapping
  • Tie strength
  • Trust aggregation

J. Golbeck

source

Google PageRank

knows

Citeseer Rank

L. Ding

J. Hendler

H. Chen

P. Kolari

knows

knows

F. Perich

T. Finin

A. Joshi

Kagal

Golbeck’s

Trust Network

hub

sink

island

sameName

Y. Peng

L. Ding

co-author

6

1

28

A. Sheth

L. Kagal

T. Finin

A. Joshi

1

5

M. P. Singh

H. Chen

F. Perich

DBLP Coauthor Network

inference web infrastructure
Inference Web Infrastructure

WWW

Toolkit

Trust computation

IWTrust

OWL-S/BPEL

SDS

(DAML/SNRC)

Proof Markup Language (PML)

End-user friendly

visualization

IW Explainer/

Abstractor

N3

CWM

(TAMI)

Expert friendly

Visualization

Trust

KIF

JTP

(DAML/NIMD)

IWBrowser

search engine

based publishing

Justification

SPARK-L

SPARK

(CALO)

IWSearch

Provenance

provenance

registration

Text Analytics

IWBase

UIMA

(NIMD/Exp Agg)

[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.

pml proof markup langauge
PML: Proof Markup Langauge

isQueryFor

IWBase

Question foo:question1

(what is Tony’s Specialty)

Query foo:query1

(type TonysSpecialty ?x)

hasAnswer

hasLanguage

Justification Trace

NodeSet foo:ns1

(hasConclusion …)

Language

hasInferencEngine

fromQuery

isConsequentOf

InferenceEngine

InferenceStep

hasRule

InferenceRule

hasAntecendent

Source

NodeSet foo:ns2

(hasConclusion …)

hasVariableMapping

Mapping

isConsequentOf

fromAnswer

hasSourceUsage

hasSource

SourceUsage

InferenceStep

usageTime …

tracking provenance via rdf molecule
Tracking Provenance via RDF Molecule

decompose

The graph’s RDF molecules

An RDF graph G

http://www.cs.umbc.edu/~dingli1

t1

foaf:knows

t2

foaf:name

t1

Li Ding

foaf:name

t2

t3

t4

Tim Finin

foaf:mbox

t3

t4

t3

mailto:finin@umbc.edu

Match sub-Graph

Web pages containing one or more molecules discovered by Swoogle

Ding, L.; Finin, T.; Peng, Y.; Pinheiro da Silva, P.; McGuinness, D.L. Tracking RDF Graph Provenance using RDF Molecules. Proceedings of the Fourth International Semantic Web Conference (poster), November 2005. 2005 , http://www-ksl.stanford.edu/KSL_Abstracts/KSL-05-06.html

conclusion
Conclusion
  • The Semantic Web
    • simple but powerful
    • Standardized by W3C: RDF, RDFS, OWL
    • Current focuses
      • Query -- SPARQL
      • Rules – SWRL, RIF
      • Web services – OWL-S, WSDL-S, SAWSDL
      • Best practice and deployment
    • but cannot do everything
  • Open questions
    • Business model, Industry adoption?
    • Privacy?
recommended readings
Recommended Readings
  • Tutorials
    • Semantic Web Road map, (since 1998), Tim Berners-Lee
    • The Semantic Web, Scientific American, May 2001, Tim Berners-Lee, James Hendler and Ora Lassila
    • Ontology Development 101: A Guide to Creating Your First Ontology, 2001, Natalya F. Noy and Deborah L. McGuinness
    • Semantic Web Tutorials, http://www.w3.org/2001/sw/BestPractices/Tutorials
  • Starting points
    • W3C Semantic Web activity, http://www.w3.org/2001/sw/
    • W3C Semantic Web Interest Group, http://www.w3.org/2001/sw/interest/
    • W3C Semantic Web News, http://www.w3.org/2001/sw/news
    • Planet RDF - aggregated blogs, http://planetrdf.com/
    • Dave Beckett’s Resource Description Framework (RDF) Resource Guide
    • Swoogle Semantic Web Search Engine, http://swoogle.umbc.edu
    • Semantic Web reference card, http://ebiquity.umbc.edu/resource/html/id/94/
  • Conferences and Journals
    • International Semantic Web Conference (ISWC)
    • European Semantic Web Conference (ESWC)
    • Semantic Technology Conference (SemTech)
    • Journal of Web Semantics
ongoing w3c s semantic web activity
Ongoing W3C’s Semantic Web Activity
  • RDF Data Access Working Group
    • RDQL…=> SPARQL
  • Rules Interchange Working Group
    • RuleML => SWRL=> RIF
  • Best Practices Working Group
    • Vocabulary management, e.g. WordNet
    • Thesauri– SKOS (Simple Knowledge Organization System)
    • Image Annotation
    • DOAP (Description of a Project)
    • Many tutorials and demos
  • Semantic Annotations for Web Services Description Language Working Group
    • OWL-S and WSDL-S
    • WSDL 2.0