Dickson K.W. Chiu

1
CSIT600f Introduction to Semantic Web

• Dickson K.W. Chiu
• PhD, SMIEEE
• Text Antoniou van Harmelen A Semantic Web
  Primer
• Ref Ivan Herman Tutorial on Semantic Web
  Technology

2
Towards a Semantic Web

• WWW is an impressive success
• amount of available information (gt 1 Giga-page)
• number of human users (gt 200 Mega-user)
• The current Web represents information using
• natural language (English, Hungarian, Chinese,)
• graphics, multimedia, page layout
• Humans can process this easily
• can deduce facts from partial information
• can create mental associations
• are used to various sensory information
• (well, sort of people with disabilities may have
  serious problems on the Web with rich media!)

3
Need for understanding Web info

• Tasks often require to combine data on the Web
• hotel and travel infos may come from different
  sites
• searches in different digital libraries
• etc.
• Again, humans combine these information easily
• even if different terminologies are used!

4
However

• However machines are ignorant!
• partial information is unusable
• difficult to make sense from, e.g., an image
• drawing analogies automatically is difficult
• difficult to combine information
• is ltfoocreatorgt same as ltbarauthorgt?
• how to combine different XML hierarchies?

5
Example Searching

• The best-known example
• Google et al. are great, but there are too many
  false hits
• adding descriptions to resources should improve
  this

6
Where we are Today the Syntactic Web
Hendler Miller 02
7
The Syntactic Web is

• A hypermedia, a digital library
• A library of documents called (web pages)
  interconnected by a hypermedia of links
• A database, an application platform
• A common portal to applications accessible
  through web pages, and presenting their results
  as web pages
• A platform for multimedia
• BBC Radio 4 anywhere in the world!
• Peer-to-peer sharing (BT, edonkey, PPLive, )
• A naming scheme
• Unique identity for those documents
• A place where computers do the presentation
  (easy) and people do the linking and interpreting
  (hard).
• Why not get computers to do more of the hard
  work?

8
Hard using the Syntactic Web

• Finding the image of something
• Find pictures that contain red birds with blue
  background
• Complex queries involving background knowledge
• Find information about animals that use sonar
  but are not either bats or dolphins
• Locating information in data repositories
• Travel enquiries
• Prices of goods and services
• Results of human genome experiments
• Finding and using web services
• Visualise surface interactions between two
  proteins
• Delegating complex tasks to web agents
• Book me a holiday next weekend somewhere warm,
  not too far away, and where they speak French or
  English

9
What is the Problem?

• Markup comprise
• rendering information (e.g., font size and
  colour)
• Hyper-links to related content
• Semantic content is accessible to humans but not
  (easily) to computers

Consider a typical web page
10
What information can we see

• WWW2002
• The eleventh international world wide web
  conference
• Sheraton waikiki hotel
• Honolulu, hawaii, USA
• 7-11 may 2002
• 1 location 5 days learn interact
• Registered participants coming from
• australia, canada, chile denmark, france,
  germany, ghana, hong kong, india, ireland, italy,
  japan, malta, new zealand, the netherlands,
  norway, singapore, switzerland, the united
  kingdom, the united states, vietnam, zaire
• Register now
• On the 7th May Honolulu will provide the backdrop
  of the eleventh international world wide web
  conference. This prestigious event
• Speakers confirmed
• Tim berners-lee
• Tim is the well known inventor of the Web,
• Ian Foster
• Ian is the pioneer of the Grid, the next
  generation internet

11
Information a machine may see

• WWW2002
• The eleventh international world wide web
  conference
• Sheraton waikiki hotel
• Honolulu, hawaii, USA
• 7-11 may 2002
• 1 location 5 days learn interact
• Registered participants coming from
• australia, canada, chile denmark, france,
  germany, ghana, hong kong, india, ireland, italy,
  japan, malta, new zealand, the netherlands,
  norway, singapore, switzerland, the united
  kingdom, the united states, vietnam, zaire
• Register now
• On the 7th May Honolulu will provide the backdrop
  of the eleventh international world wide web
  conference. This prestigious event
• Speakers confirmed
• Tim berners-lee
• Tim is the well known inventor of the Web,
• Ian Foster
• Ian is the pioneer of the Grid, the next
  generation internet

12
Solution XML markup with meaningful tags?
ltnamegtWWW2002 The eleventh international world
wide webconlt/namegt ltlocationgtSheraton waikiki
hotel Honolulu, hawaii, USAlt/locationgt
How about ltconfgtWWW2002 The eleventh
international world wide webconlt/confgt ltplacegtSher
aton waikiki hotel Honolulu, hawaii, USAlt/placegt
Then how about lt??gtWWW2002 The eleventh
international world wide webconlt/??gt lt??gtSheraton
waikiki hotel Honolulu, hawaii, USAlt/??gt
13
What Is Needed?

• A resource should provide information about
  itself
• also called metadata
• metadata should be in a machine processable
  format
• agents should be able to reason about
  (meta)data
• metadata vocabularies should be defined

14
What Is Needed (Technically)?

• To make metadata machine processable, we need
• unambiguous names for resources (URIs)
• a common data model for expressing metadata (RDF)
• and ways to access the metadata on the Web
• common vocabularies (Ontologies)
• The Semantic Web is a metadata based
  infrastructure for reasoning on the Web
• It extends the current Web (and does not replace
  it)

15
Adding Semantics

• External agreement on meaning of annotations
• E.g., Dublin Core (http//dublincore.org/)
• Agree on the meaning of a set of annotation tags
• Problems with this approach
• Inflexible
• Limited number of things can be expressed
• Use Ontologies to specify meaning of annotations
• Ontologies provide a vocabulary of terms
• New terms can be formed by combining existing
  ones
• Meaning (semantics) of such terms is formally
  specified
• Can also specify relationships between terms in
  multiple ontologies

16
History of the Semantic Web

• Web was invented by Tim Berners-Lee (amongst
  others), a physicist working at CERN
• TBLs original vision of the Web was much more
  ambitious than the reality of the existing
  (syntactic) Web
• TBL (and others) have since been working towards
  realising this vision, which has become known as
  the Semantic Web
• E.g., article in May 2001 issue of Scientific
  American

... a goal of the Web was that, if the
interaction between person and hypertext could be
so intuitive that the machine-readable
information space gave an accurate representation
of the state of people's thoughts, interactions,
and work patterns, then machine analysis could
become a very powerful management tool, seeing
patterns in our work and facilitating our working
together through the typical problems which beset
the management of large organizations.
17
Berner-Lees Architecture
? Semanticsreasoning
?
? Relational Data
?
? Data Exchange

• Relationship between layers is not clear
• OWL DL extends DL subset of RDF

18
A Spectrum of Ontology
Thesauri narrower term relation
Frames (properties)
General Logical constraints
Formal is-a
Catalog/ ID
Informal is-a
Formal instance
Disjointness, Inverse, part-of
Terms/ glossary
Value Restrs.
19
Ontology Origins and History

• Ontology in Philosophy - a philosophical
  disciplinea branch of philosophy that deals with
  the nature and the organization of reality
• Science of Being (Aristotle, Metaphysics, IV, 1)
• studies being or existence as well as the basic
  categories thereof
• trying to find out what entities and what types
  of entities exist
• has strong implications for the conceptions of
  reality.

20
Ontology in Linguistics
Tank
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Ontology in Computer Science

• An ontology is an engineering artifact
  Neches91
• defines basic terms and relations comprising the
  vocabulary of a topic area
• the rules for combining terms and relations to
  define extensions to the vocabulary
• An explicit specification of a
  conceptualization Gruber93
• Formal specification of a shared
  conceptualization (of a certain domain) Borst
  97
• Shared understanding of a domain of interest
• Formal and machine manipulable model of a domain
  of interest

22
Structure of an Ontology

• Ontologies typically have two distinct
  components
• Names for important concepts in the domain
• Elephant is a concept whose members are a kind of
  animal
• Herbivore is a concept whose members are exactly
  those animals who eat only plants or parts of
  plants
• Adult_Elephant is a concept whose members are
  exactly those elephants whose age is greater than
  20 years
• Background knowledge/constraints on the domain
• Adult_Elephants weigh at least 2,000 kg
• All Elephants are either African_Elephants or
  Indian_Elephants
• No individual can be both a Herbivore and a
  Carnivore

23
Ontology Elements

• Concepts (classes) their hierarchy
• Concept properties (slots / attributes)
• Property restrictions (type, cardinality, domain,
  etc.)
• Relations between concepts (disjoint, equality,
  etc.)
• Instances
• E-R diagram / UML diagram ???
• Note Property ? Slot ? Relation ?
  Relationtype ? Attribute ? Semantic link
  type

24
A Semantic Web First Steps
Make web resources more accessible to automated
processes

• Extend existing rendering markup with semantic
  markup
• Metadata annotations that describe
  content/function of web accessible resources
• Use Ontologies to provide vocabulary for
  annotations
• Formal specification is accessible to machines
• A prerequisite is a standard web ontology
  language
• Need to agree common syntax before we can share
  semantics
• Syntactic web based on standards such as HTTP and
  HTML

25
Ontology Design and Deployment

• Given key role of ontologies in the Semantic Web,
  it will be essential to provide tools and
  services to help users
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captured intuitions of domain experts
• Minimally redundant no unintended synonyms
• Richly axiomatized (sufficiently) detailed
  descriptions
• Store (large numbers) of instances of ontology
  classes, e.g.
• Annotations from web pages
• Answer queries over ontology classes and
  instances, e.g.
• Find more general/specific classes
• Retrieve annotations/pages matching a given
  description
• Integrate and align multiple ontologies

26
Steps in building an ontology

• determine domain and scope
• enumerate important terms
• define classes and class hierarchies
• define slots
• define slot constraints (cardinality, value-type,
  etc.)

27
Step 1 Determine Domain and Scope
Application route planning agent
Possible questions Distance between two
cities? What sort of connections exist between
two cities? In which country is a city? How many
borders are crossed?
28
Step 2 Enumerate Important Terms
29
Step 3 Define Classes and Class Hierarchy
30
Step 4 Define Slot of Classes
Step 5 Define Slot Constraints

• Attribute cardinality
• Ex Borders_with multiple, Start_point single

• Attribute-value type
• Ex Borders_with- Country

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Issues on class hierarchy
- all is-a relations hold? Inst(B) ? Inst(A)
32
Issues on Slots
33
More Example Automatic Assistant

• Your own personal (digital) automatic assistant
• knows about your preferences
• builds up knowledge base using your past
• can combine the local knowledge with remote
  services
• hotel reservations, airline preferences
• dietary requirements
• medical conditions
• calendaring
• etc
• It communicates with remote information (i.e., on
  the Web!)

34
Example Database Integration

• Databases are very different in structure, in
  content
• Lots of applications require managing several
  databases
• after company mergers
• combination of administrative data for
  e-Government
• biochemical, genetic, pharmaceutical research
• etc.
• Most of these data are now on the Web
• The semantics of the data(bases) should be known
• how this semantics is mapped on internal
  structures is immaterial

35
Example Digital Libraries

• It is a bit like the search example
• It means catalogs on the Web
• librarians have known how to do that for
  centuries
• goal is to have this on the Web, World-wide
• extend it to multimedia data, too
• But it is more software agents should also be
  librarians!
• help you in finding the right publications

36
Example Semantics of Web Services

• Web services technology is great
• But if services are ubiquitous, searching issue
  comes up, for example
• find me the most elegant Schrödinger equation
  solver
• what does it mean to be
• elegant?
• most elegant?
• mathematicians ask these questions all the time
• It is necessary to characterize the service
• not only in terms of input and output parameters
• but also in terms of its semantics

37
How Simple Ontologies Help

• not as costly to build and potentially
• more importantly, many are available
• provide a controlled vocabulary
• website organization and navigation support
• support expectation setting (e.g. user interface)
• umbrella structures from which to extend
  content (e.g., UNSPSC)
• searching support
• sense disambiguation support (e.g., terms belong
  to different categories)

Deborah McGuinness. Ontologies Come of Age. The
Semantic Web Why, What and How, MIT Press, 2001.
(MS-Word)
38
How Structured Ontologies Help

• more structure gt more power
• consistency checking
• completion (of unspecified attributes and
  relations)
• interoperability support
• validation and verification testing or even
  encode entire test suites
• structured, comparative, and customized search
• intelligence in application, e.g., system
  configuration support

39
Benefits of Semantic Web

• Communication between people
• Interoperability between software agents
• Reuse of domain knowledge
• Make domain knowledge explicit
• Analyze domain knowledge

40
The Semantic Web is Not

• Artificial Intelligence on the Web
• although it uses elements of logic
• it is much more down-to-Earth (we will see
  later)
• it is all about properly representing and
  characterizing metadata
• of course AI systems may use the metadata of the
  SW
• but it is a layer way above it
• A purely academic research topic
• SW is out of the university labs now
• lots of applications exist already (see examples
  later)
• big players of the industry use it (Sun, Adobe,
  HP, IBM,)
• of course, much is still be done!
• Building an ontology is not a goal in itself