The Semantic Web -- an overview --

1
The Semantic Web -- an overview --

• Dr Yuri A. Tijerino
• Computer Science Department
• Brigham Young University

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The Book of Genesis tells of a great tower built
by men not only from fear of a second Flood but
above all to make a name for themselves. Gods
punishment was the Babylonian confusion of
tongues, with men unable to understand each
other, the result being that the tower was never
finished.
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Todays Web

• information overload
• massive, heterogeneous data sets
• unstructured documents (e.g. e-mails)
• lack of context and meaning
• new forms of content
• software programs, sensors, ambient devices
• blur between content services
• mixing Web content with smart executables

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Limitations of the Web Today
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The Semantic Web

• Tim Berners-Lee
• an extension of the current web in which
  information is given well-defined meaning, better
  enabling computers and people to work in
  cooperation
• An open platform allowing information to be
  shared and processed automatically
• adding context and structure via metadata

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The Agent Computing Paradigm

• The old way of thinking about computer programs
  a program
• begins executing
• takes input
• gives output
• finishes executing
• The new way programs
• interact with each other
• are always active
• should be robust (ie, able to deal with the
  unexpected)

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From Agents to Knowledge Markup

• Almost everything we need to know is on the web.
• What a great resource for agents!
• But Agents dont understand web pages.
• Natural Language processing is too hard for
  computers, and will remain so for a long time.
• The solution Knowledge Markup.

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Knowledge Markup in a Nutshell

• A web page describes objects.
• Datasets, human beings, services, items for sale,
  etc.
• The semantics of an object are defined by the
  place it occupies in some domain ontology.
• The basic idea of knowledge markup is to use
  ontologies to markup a web page according to the
  location its objects occupy in the ontology.
• Essentially, knowledge markup is knowledge
  representation done in ontologies.

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Benefits of Knowledge Markup

• Agents can parse a page, and immediately
  understand its semantics.
• No need for natural language processing.
• Searches can be done on concepts. The inheritance
  mechanisms of the back-end knowledge base obviate
  the need for keywords.
• Data and knowledge sharing.

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Knowledge Markup Example (Hypothetical)

• You ask the system Show me all universities near
  the beach.
• The UCLA page doesnt say anything about the
  beach, but it does say (through knowledge markup)
  that its near the Pacific Ocean.
• UCLA makes use of a geography ontology which
  includes the rule Ocean(x) ?hasBeaches(x).
• When your search agent parses the UCLA page, it
  loads in the relevant ontologies, deduces that
  UCLA is near the beach, and returns the page.

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Semantic Web Vision
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XML is a first step

• Semantic markup
• HTML ? layout
• XML ? meaning
• Metadata
• within documents
• not across documents

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XML example

• ltplaygt
• lttitlegtThe Life and Death of King Johnlt/titlegt
• ltDramatis Personaegt
• ltpersonagtThe Earl of PEMBROKElt/personagt
• ltpersonagtThe Earl of ESSEXlt/personagt
• lt/Dramatis Personaegt
• ltStagedirgtSCENE England, the
  Court.lt/Stagedirgt
• ltactgtAct 1
• ltscenegtScene I.
• ltspeechgt
• ltspeakergtJOHNlt/speakergt
• ltlinegtNow, Chatillon, what would
  France with us?lt/linegt
• lt/speechgt

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Resource Description Framework (RDF)

• A standard of W3C
• Relationships between documents
• (or parts of documents)
• Can be an XML application
• Consisting of triples or sentences
• subject
• property or predicate (verb)
• object
• RDF RDFS used to define ontologies

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A simple example

• Tolkein wrote The Lord of the Rings
• hasWritten (http//www.famouswriters.org/tolkein/
  , http//www.books.org/ISBN00001047582
  /)

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RDF Schema

• RDF Schema is a frame based language used for
  defining RDF vocabularies.
• Introduces properties rdfssubPropertyOf and
  rdfssubClassOf
• Defines semantics for inheritance and
  transitivity.
• Introduces notions of rdfsDomain and rdfsRange
• Also provides rdfsConstraintProperty

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RDF Schema Lexicon
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The Recapitulation of AI Research

• The last 5 years have seen a recapitulation of 40
  years of AI history.
• Data Structures ?XML
• Semantic Networks ?RDF
• Early Frame Based Systems ?RDFS
• As a mechanism for metadata encapsulation,
  RDFS works just fine. But it is unsuited for
  general purpose knowledge representation. This is
  where the AI community steps in, saying,
  essentially, We know how to do this please let
  us help.

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What are Ontologies?

• Ontologies provide a shared and common
  understanding of a domain
• a (shared) specification of a conceptualisation
• concept map
• a simple example - Yahoo
• BusinessEconomy gt Finance gt Banking
• universal, non-consensual, manual, changes slowly
• for WWW, defined using RDF-Schema (RDFS)

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A History of Knowledge Representation

• Knowledge representation (KR) is the branch of
  artificial intelligence (AI) that deals with the
  construction, description and use of ontologies.
• How do we model a domain for input into the
  machine?
• Ontology is the branch of philosophy that answers
  the question
• what is there?
• Some big names in ontology Parmenides, Plato,
  Aristotle, Kant, Pierce, Husserl
• For a program to reason, it must have a
  conceptual understanding of the world. This
  understanding is provided by us. Thus we have to
  answer questions that weve been considering for
  several thousand years.
• Today, in computer science, an ontology is
  typically a hierarchical collection of classes,
  permissible relationships amongst those classes,
  and inference rules

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Ontology as Taxonomy
Computer Networks
Distributed Systems
Network Architectures
ISDN
Wireless Communication
ATM
Client/ Server
Network OS
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Ontology of People and their Roles
Employee
Expert
Analyst
Manager
Programme Mgr
Project Mgr
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Ontology and Logic

• Reasoning over ontologies
• Inferencing capabilities
• X is author of Y ? Y is written by X
• X is supplier to Y Y is supplier to Z ?
• X and Z are part of the same
  supply chain
• Based on Description Logic research

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Example Ontology (Scientific Pedagogy)

• Classes
• Experimental science (ES)
• Theoretical science (TS)
• Good Teaching Example (GTE)
• Relationships
• Motivates A particular instance of TS may
  motivate an instance of ES.
• Demonstrates A particular instance of ES may
  demonstrate an instance of TS.
• Inference Rules
• ES(X) and TS(Y) and Demonstrates(X,Y) ?GTE(X,Y)

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The DAML Program

• DAML DARPA Agent Markup Language
• Defense Advanced Research Agency (DARPA) program
• Program Managers James Hendler, Murray Burke
• Begin in August 2000
• Goal achieve semantic interoperability between
  Web pages, databases, programs, and sensors
• Integration contractor and 16 technology
  development teams
• MIT (Tim Berners-Lee, Ben Grosof)
• Stanford (Gio Weiderhold, Richard Fikes, Deborah
  McGuinness)
• UMBC (Tim Finin)
• U West Florida (Pay Hayes)
• Yale (Drew McDermott)
• Advisors Ramanthan Guha, Peter Patel-Schneider,

• Web site http//www.daml.org/

• Cycorp (Doug Lenat)
• Nokia (Ora Lassila)
• Teknowledge (Bob Balzer)

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DAMLOIL OWL

• A representation language for user-defined
  ontologies
• An ontology added to RDF and RDF-Schema
• Specification document
• http//www.daml.org/2001/03/damloil-index.html
• Expressive power analogous to
• Description logics (e.g., CLASSIC)
• Monotonic frame languages (e.g., OKBC knowledge
  model)
• Designed in collaboration with the European
  Community
• Designers of the Ontology Inference Layer (OIL)
• Basis for Web Ontology Language (OWL), the
  candidate W3C standard

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DAMLOIL Classes

• Thing
• Restriction
• List
• Ontology
• AbstractProperty
• TransitiveProperty
• DatatypeProperty
• UniqueProperty
• UnambiguousProperty
• Nothing

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DAMLOIL Properties

• Classes
• disjointWith
• Defining Non-primitive classes
• unionOf, disjointUnionOf, intersectionOf,
  complementOf, oneOf
• Restrictions
• onProperty, toClass, hasValue, hasClass,
  hasClassQ
• minCardinality, maxCardinality, cardinality
• minCardinalityQ, maxCardinalityQ, cardinalityQ

• Equivalence
• equivalentTo, sameClassAs, samePropertyAs
• Lists
• first, rest, item
• Properties
• inverseOf
• Ontologies
• versionInfo, imports

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Property Restrictions on Classes
ltClass ID "Person"gt ltcommentgt Person is a
subclass of objects whose parents are persons.
lt/commentgt ltrdfssubClassOfgt
ltdamlRestrictiongt ltdamlonProperty
rdfresource hasParent /gt
ltdamltoClass rdfresource Person /gt
lt/damlRestrictiongt lt/rdfssubClassOfgt
ltcomment gt Person is a subclass of resources
that have one father. lt/commentgt
ltrdfssubClassOfgt ltdamlRestrictiongt
ltdamlonProperty rdfresource hasFather
/gt ltdamlcardinalitygt 1
lt/damlcardinalitygt lt/damlRestrictiongt
lt/rdfssubClassOfgt
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Comments on DAMLOIL (OWL)

• Expressive power of a description logic
• Representation language for both classes and
  instances
• Additional expressive power needed (at least FOL)
• No rationale for excluding any axiom from an
  ontology that is
• Not a tautology
• Satisfied by the intended interpretation of the
  ontology
• Example of need for additional expressive power
• The magnitude of a physical quantity in a given
  unit of measure
• (gt (AND (Quantity-Magnitude ?q ?u ?m)
  (Quantity-Dimension ?q ?d))
• (AND (type Physical-Quantity ?q) (type
  Unit-Of-Measure ?u)
• (type Magnitude ?m)
  (Unit-Dimension ?u ?d)))
• May be too difficult for the Web community to
  understand
• Acceptance will be depend on user-friendly tools
• Ok to support development of Semantic Web
  technology

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Issues Facing OWL Need for Really Good
Annotation Tools

• OWL is not meant to be read or written by human
  beings.
• Humans will make assertions through intuitive
  user interfaces, which will generate the
  appropriate OWL markup.
• In fact, the markup should fall out of the
  activity of building a web page.
• This requires some thought.

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Proof and Trust
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Example 1 Focused Crawling

• Special purpose search engines will increasingly
  replace all-purpose engines.
• The notion of an all-purpose search engine is
  yielding to that of special-purpose engines.
• Such engines do not want to index irrelevant
  pages.
• Current focused crawling techniques employ
  heuristics based on text mining, and
  collaborative filtering.
• A cleaner approach would be for web sites to
  describe themselves with RDF or DAML.
• An entire site map could be expressed in RDF,
  along with metadata descriptions of each node in
  the map.
• An agent would know precisely which of the sites
  pages are worth checking out.

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Example 2 Indexing the Hidden Web

• Search engines google, infoseek, etc. work by
  constantly crawling the web, and building huge
  indexes, with entries for every word encountered.
• But a lot of web information is not linked to
  directly. It is hidden behind forms.
• eg www.allmovies.com allows you to search a vast
  database of movies and actors. But it does not
  link to those movies and actors. You are required
  to enter a search term.
• A web-spider, not knowing how to interact with
  such sites, cannot penetrate any deeper than the
  page with the form.

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Indexing the Hidden Web (Contd.)

• Now imagine that allmovies.com had some DAML
  attached, which said
• I am allmovies.com. I am an interface to a
  vast database of movie and actor information. If
  you input a movie title into the box, I will
  return a page with the following information
  about the movie If you input an actor name, I
  will return a page with the following information
  about the actor

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Indexing the Hidden Web (Contd.)

• An OWL aware spider can come to such a page and
  do one of two things
• If it is a spider for a specialized search
  engine, it may ignore the site altogether.
• If not, it can say to itself I know some movie
  titles. Ill input them (being careful not to
  overwhelm the site), and index the results (and
  keep on spidering from the result pages).
• At the least, the search engine can record the
  fact that
• www.allmovies.com/execperson?namex returns
  information about the actor with name x.

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Example 3 Knowledge Sharing/Corporate Memory

• Our problem The Church is growing, and various
  organizations, departments and divisions need to
  collaborate and share knowledge.
• The wheel often gets reinvented.
• Our proposal
• Build an ontology which captures gospel, family
  history, education and other relevant knowledge.
• Mark up talks, scriptures, curriculum materials,
  etc. according to this ontology.
• Harvest the information with OWL aware
  web-crawlers.
• Build OWL aware query agents.

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Example 3 (Contd.)

• Leaders and members should be able to tell the
  query agent the current form of their data (e.g.
  a articles and a subject), their desired output
  (e.g. all other related lessons prepared by
  others), and get back the series of available
  lessons and advice necessary to prepare the
  lesson.
• We also have a chicken and egg problem here.
• Leaders and members dont want to invest time in
  yet another knowledge technology.
• How do we do it?

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DAML Example 4 ittalks.org

• www.ittalks.org will be a repository of
  information about information technology (IT)
  talks given at universities and research
  organization across America.
• A users information (research interests,
  schedule, constraints, etc.) will be stored on
  their personal DAML page.
• When a new talk is added, the personal agents of
  interested users will be notified.
• The personal agents will determine, based on
  schedule, driving time, more refined interest
  specifications, etc, whether or not to inform the
  user.

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ittalks.org (Contd.)

• Example Scenario
• You are going to be in Boston for a few days. You
  enter this in your schedule, and you are
  automatically notified of several talks, at
  several Boston universities, that match your
  interests. You select one that you would like to
  attend. You get a call on your cell-phone letting
  you know when it is time to leave for the talk.

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The Road Ahead

• Enormous synergy between KM, ubiquitous
  computing, and agents.
• Start Trek, here we come.
• The concept is clear, but many details need to be
  worked out.
• Semantic Web systems can be built incrementally.
• Start small. Even a very modest effort can
  massively improve search results.

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What Gartner says ...

• To 2004, significant Semantic Web activity in
  improving information access for eBusiness
• To 2006, broader-scale success is still
  uncertain for several reasons
• potentially prolonged poor economic climate
• lack of clear business models around ontologies
• but much sector-specific activity
• overhead and complexity of creating and
  maintaining ontologies
• Despite this, information-intensive enterprises
  should definitely do more than just take note.

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Conclusions

• To conclude
• The first version of the Web lacked a metadata
  framework which was needed to describe resources
• W3C developed RDF to provide this framework
• As well as providing an framework for metadata
  applications, RDF allows software to reach beyond
  individual Web sites
• The Semantic Web will be based on registries of
  machine-understandable definition
• The Semantic Web standard language (OWL) was
  derived from US and EC efforts (DAMLOIL)
• The Semantic Web will be difficult to achieve
• It will be expensive to provide rich
  interoperable services without a Semantic Web

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Find Out More (1)

• Semantic Web, W3Clthttp//www.w3c.org/2001/sw/gt
• Semantic Web Road map, Tim Berners-Leelthttp//www
  .w3c.org/DesignIssues/Semantic.htmlgt
• The Semantic Web, Scientific Americanlthttp//www.
  sciam.com/2001/0501issue/0501berners-lee.htmlgt
• The Semantic Web Community Portal,
  lthttp//www.semanticweb.org/gt
• The Semantic Web A Primerlthttp//www.xml.com/pub
  /a/2000/11/01/semanticweb/gt
• All found using Google to search for semantic
  Web

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Find Out More (2)

• An introduction to RDF
• lthttp//www-106.ibm.com/developerworks/xml/librar
  y/w-rdf/gt
• The Semantic Web Community Portal
• lthttp//www.semanticweb.org/gt
• The Semantic Web An Introduction
• lthttp//infomesh.net/2001/swintro/gt