Semantic Web: The Basics

1
Semantic WebThe Basics

• Heiner Stuckenschmidt
• University of Mannheim

2
The Original Vision

• Berners-Lee, Hendler, Lassila The Semantic Web,
  Scientific American, May 17th 2001
• Cited (and abused) extensively in literature and
  science marketing

3
Key Ideas

• Smart Devices
• Personal Information Agents
• Knowledge about objects, time and space
• Trusted Information

4
Key Technologies

• Machine-Readable Metadata
• Based on XML and RDF
• Logic, Inference Rules and Proofs
• Ontologies
• Agent Technologies (nowadays read web
  services)

5
Why not use Google ?

• Relevant pages instead of answers
• Weak filtering, lots of irrelevant information
• Biased by popularity of pages
• Hard to pose general questions
• No way to ask about types of objects
• Not possible to use relations

6
Required are

• a standard syntax,
• so meta-data can be recognised as such
• one or more standard vocabularies
• so search engines, producers and consumersall
  speak the same language
• lots of resources with meta-data attached

7
RDF Resource Description Framework

• RDF is a data model
• used to describe meta-data of a piece of data
• not a language, like XML
• although it has an XML syntax (but also other)
• Benefits
• Unique representations of content objects
• Explicit relations between resources

photo
last-name
http//ki.informatik.uni-mannheim.de/people/heine
r
Stuckenschmidt
gives-lecture
title
http//ki.informatik.uni-mannheim.de/courses/sw
Semantic Web Technologies
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RDF Schema
(X R Y), (R subPropertyOf Q) ? (X Q Y) (X R Y),
(R domain C) ? (X type C) (X type C), (C
subClassOf D) ? (X type D)
Person
subClassOf
domain
range
Teacher
Course
teaches
subPropertyOf
type
gives-lecture
type
RDF schema
RDF data
teaches
http//ki.informatik.uni-mannheim.de/people/hein
er
http//ki.informatik.uni-mannheim.de/courses/sw
gives-lecture
9
Problem no semantic guarantees
Source B
Does_consultancy_for
range
domain
type
www.bigcompany.com
Company
Does_consultancy_for
type
Source A
http//ki.informatik.uni-mannheim.de/people/hein
er
http//ki.informatik.uni-mannheim.de/courses/sw
gives-lecture
10
Logical Reasoning about Resources
Ontology

• Logical Axioms limit allowed interpretations

Thing
Person
Course
Company
Teacher ? Person ? Thing ? Company ? Company
Teacher
Teacher ? Person ? Thing ? Company
Teacher ? ?teaches.Course Teacher ?
Person Person ? Thing ? Company
Teacher ? Person
Company
Teacher
type
http//ki.informatik.uni-mannheim.de/people/hein
er
http//ki.informatik.uni-mannheim.de/courses/sw
teaches
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Semantic Web Service

• Semantic WS - semantically annotated WS to
  automate discovery, composition, execution
• Example

lt rdfIDWS1"gt
ltowlshasInput rdfresource /gt
ltowlshasInput rdfresource
/gt ltowlshasOutput
rdfresource
/gt lt/ gt
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OWL-S A Service Ontology

• OWL-S is an OWL ontology for describing services.
  
• It can be combined with domain ontologies
  describing the meaning of the data processed by a
  service

Service
Service Profile
Service Model
Service Grounding
How to access it data models and communication
What it does functionality and tasks
How it works behavior of the service
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Service vs. Ontology
OWL Ontology
OWL Ontology
Profile Ontology
OWL-S
E_Commerce
Profile
Service
Process
Grounding
Book_Selling
Airline_Ticketing

• Domain Ontology
• Airport
• Flight Itinerary
• confirmation
• Reservation Number

OWL Ontology
Profile-Description BravoAir is an airline
ticketing service Input departure and arrival
airport Output confirmation and flight
itinerary or reservation number
Instance
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Domain Ontology OWL Web Ontology Language
Base Technologies RDF, RDF Schema
Service Semantics OWL-SService Ontology
Descriptions WSDL Web Service Description
Language
Base Technologies XML, XML Schema
Messages SOAP Simple Object Access Protocol
Communication HTTP Hypertext Transfer Protocol
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We need

• a standard syntax,
• so meta-data can be recognized as such
• one or more standard vocabularies
• so search engines, producers and consumersall
  speak the same language
• lots of resources with meta-data attached

16
standard vocabularies (Ontologies)

• Identify the key concepts in a domain
• Identify a vocabulary for these concepts
• Identify relations between these concepts
• Make these precise enough so that they can be
  shared between
• humans and humans
• humans and machines
• machines and machines

17
Standardized Vocabularies
18
Classifications
19
Web Directories
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Thesauri

• Many Examples
• General Wordnet, Roget
• Famous Persons ULAN
• Geography Getty Thesaurus
• Arts and Architecture AAT
• Medicine UMLS (gt 50 thesauri)
• Environmental Protection Gemet
• ...

21
Ontologies
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Role of ontologies Content explication

• Single ontology approaches
• Same view on domain necessary, problematic when
  information source changes, minimal ontology
  commitment hard to find
• Multiple ontology Approach
• Different Sources use different conceptualization
  that are represented in different ontologies

Globalontology
DB
DB
DB
Local ontology
Local ontology
Local ontology
DB
DB
DB
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The Bottom Line Ontology Alignment
Accommodation
Accommodation
Hotel
Rentals
Hotel
Apartment
4StarHotel
First-Class-Hotel
Apartment
5StarHotel
Bungalow
Congress-Hotel
Hostel
Bed Breakfast
24
We Need

• eine Standard Syntax,
• Damit Metadaten als solche erkannt werden
• Ein oder mehrere standardisierte Vokabulare
• So dass Suchmaschinen und Informationsanbieter
  die selben Begriffe benutzen
• Mit Metadaten annotierte Informationsinhalte

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Statistical Indexing
26
Natural Language Processing
27
Where are we now
ontology
edit
extract
instances
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Living in the Real Web

• The Technical Level
• Distributed Information
• P2P-like Architecture (mobile devices, sensor
  networks)
• Possible Failures (network congestion, lack of
  power)
• The Content Level
• Inconsistency and Incompleteness
• Heterogeneity and Ontology Alignment
• Multimedia Information Extraction

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Where it breaks
ontology
edit
generate
import
instances
30
So your ontologies are distributed

• current reasoners
• Global ontology
• Reason in global
• ontology
• Problems
• Scalability?
• Reasoning specificity?
• Privacy? Autonomy?
• Robustnes?

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your Reasoning should be as well

• Alternative approach
• Local reasoning
• Suitable combination
• Requirements
• Formal framework
• Reasoning algorithm
• The reason-able
• system implementation

32

• Similarly to OWL-DL
• Core reasoning task in DDL concept subsumption
• Difference from OWL-DL
• Scope Galaxy
• Mappings matter
• Subsumption in DDL
• a global subsumption

33
Where do the mappings come from ?

• Decompositional Approach
• One existing Ontology is split into different
  ones
• Mappings arise naturally from decomposition
• Compositional Approach
• Different Ontologies exist
• Mappings have to be found based on semantic
  correspondences

34
Ontology Matching
35
Problems Partial Matching

• Sloppy terminologies need robust inference

subClassOf
36
Description Logics

• Concepts Represent Sets of Instances with common
  properties Forest, Mixed-Forest
• Concept expressions define common properties

37
Subsumption Reasoning

• C is a special case of D
• Holds for C Mixed-Forest and D Vegetation
• Does not hold for C Mixed-Forest and D Forest

38
Approximate Subsumption

• Based on corresponding satisfiability problem
  with respect to a non-standard interpretation
• Used for Partial Matching

Forests subsume Mixed-Forest if you disregard
shrubs
39
Result Mappings

• Mapping elements are 5-Tuples (id,e,e,R,n)
• id is a unique identifier for a given mapping
  element
• e and e are entities in the mapped ontologies
• R is a relation that holds between the elements
• n is a confidence measure for the mapping
• Two possible readings the measure
• Degree to which the entities relate , but also
• Confidence that the result of the matching is
  correct

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Problem Wrong Mappings

• Example 1 Matching Fallacies
• iAuthor ? Person
• jAuthorization ? ?Person
• iPerson jPerson
• iAuthor jAuthorization
• Example 2 Modelling Fallacies
• i SportsCar ? Car
• jUselessThings ? ?UsefulThings
• iCar j UsefulThing
• iSportsCar j ?UsefulThing

?
?
?
?
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Reasoning ABOUT Mappings

• Check formal properties of mappings
• Are there inconsistent mappings
• Are there redundant mappings
• Are there implied mappings
• Use Cases
• Support for manual mapping creation
• Validation of automatically created mappings

42
Example 1 Inconsistency
AuthorizationI2 ?
T1
T2
Author
Authorization ? ?Person
isA
Person
Person
AuthorizationI2 ?
r12(AuthorizationI1)
r12(PersonI1) ? PersonI2
?
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Example 2 Embedding
r12(SportsCar) ?
T1
T2
SportsCar
UselessThings
isA
Car
UseFullThings
r12(SportsCarI1) ? UselessThingsI2

r12(SportsCarI1) ? r12(CarI1) ? UseFullThingsI2
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Repairing Mappings
T1
T2
?
(n 0.47)
Author
Authorization ? ?Person
(n 1.0)
isA
(n 0.47)
Person
Person
?
(n 1.0)

• Syntactic Matching

2. Structural Matching
3. Analysis
45
Repairing Mappings
T1
T2
?
(n 0.47)
Author
Authorization ? ?Person
(n 1.0)
isA
(n 0.47)
Person
Person
?
(n 1.0)

• Syntactic Matching

4. Compute Conflict Sets
2. Structural Matching
5. Select Problematic Rules and repair mapping
3. Analysis
46
The typical Web Application 2010 (?)
47
Conclusions

• So what is wrong with the semantic web so far ?
• A lot of work was done on language for describing
  rich information semantics (which is good!)
• Too little attention has been paid to the
  specific needs of a distributed and heterogeneous
  environments such as the Web (this is not so
  good)
• Is it any good then ?
• YES. There are many useful applications with a
  rather centralized nature (community portals,
  company web sites)
• YES. People start recognizing the need for
  distributed and robust approaches.

48
Acknowledgement

• Thank you for attending !
• During the Presentation, I used material
  originally created by
• Luciano Serafini, Andrei Tamilin, ITC-IRST Trento
• Jerome Euzenat, INRIA Rhones-Alpes
• Pavel Shvaiko, Fausto Giunchiglia, University of
  Trento
• The DRAGO System for distributed reasoning with
  ontologies can be downloaded at
  http//sra.itc.it/projects/drago/
• This work is partially funded by the German
  Science Foundation in the Emmy-Noether Programme