1. Semantic Web The Story So Far
Ian Horrocks
Oxford University
Computing Laboratory

2. The Semantic Web

3. What is it? “… a consistent logical web of data …”
Web “invented” by Tim Berners-Lee (amongst others)
(Conceptual) simplicity of web has contributed to success, but is also a limiting factor
Tim has ambitious goals for future of the web
Objective is to overcome existing limitations
This vision of the future of the Web has become known as the Semantic Web
“… a consistent logical web of data …”
“… information is given well-defined meaning …”
- Even if he didn’t invent it, TBL was a key figure in the development of the Web.
- This is what he originally intended, and what he would like to see the web become.

4. Why do we want it?
Many tasks are difficult or impossible using existing web:
Concrete motivation for why we want the semantic web - some tasks are hard work using existing web, e.g:
Find images of FvH, PFPS and Alan Rector
Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

5. Why do we want it?
Many tasks are difficult or impossible using existing web:
Complex queries involving background knowledge
Find information about “animals that use sonar but are neither bats nor dolphins”
Locating information in data repositories
Travel enquiries
Prices of goods and services
Results of human genome experiments
Finding and using “web services”
Given DNA sequence, identify genes, determine proteins they produce, and hence biological processes they control
, e.g., Barn Owl
Workflows widely used in eScience; hard to (automatically) find relevant services
Can think of final bullet as a sort of grand challenge; web equivelent of robot football

6. What is the Problem? Consider a typical web page: Markup consists of:
rendering information (e.g., font size and colour)
Hyper-links to related content
Semantic content is accessible to humans, but not (easily) to computers…
- Information in natural language, and even embedded in images.
- And if this is a problem for web pages, what about hypermedia, databases and other web resources?

7. How Will It Work? Add semantic annotations to web resources
NL annotations (possibly with rendering annotation) already associated with images (only way google can find them)
Augment NL with semantic annotation.
Dr. Alan Rector, Professor of Computer Science, University of Manchester
Dr. <Person>Alan Rector</Person>, <Job>Professor of Computer Science</Job>, University of Manchester
Rev. <Person>Alan M. Gates</Person>, <Job>Associate Rector</Job> of the Church of the Holy Spirit, Lake Forest, Illinois
Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

8. How Will It Work? Now... that should clear up a few things around here
NL annotations (possibly with rendering annotation) already associated with images (only way google can find them)
Augment NL with semantic annotation.

9. Giving Semantics to Annotations
Agree on meaning of a set of annotation tags
E.g., Dublin Core
Limited flexibility and extensibility
Limited number of things can be expressed
Agree on language used to define meanings
E.g., an ontology language
Flexible and extensible
New terms can be formed by combining existing ones
Meaning (semantics) of such terms is formally specified
Big Apple is a large fruit or a city?

10. The Web Ontology Language OWL
Development and standardisation of (Semantic) Web ontology languages has been responsible for huge increase in the development and application of ontologies.

11. Web Ontology Language OWL
Semantic Web led to requirement for a “web ontology language”
set up Web-Ontology (WebOnt) Working Group
WebOnt developed OWL language
OWL based on earlier languages RDF, OIL and DAML+OIL
OWL now a W3C recommendation (i.e., a standard)
OWL is a family of 3 languages: OWL Lite, OWL DL and OWL Full
OIL, DAML+OIL and OWL (DL & Lite) based on Description Logics
Has facilitated development of wide range of high quality tools & infrastructure
OWL now language of choice in many applications

12. What Are Description Logics?
A family of logic based Knowledge Representation formalisms
Descendants of semantic networks and KL-ONE
Describe domain in terms of concepts (classes), roles (properties, relationships) and individuals
Operators allow for composition of complex concepts
Names can be given to complex concepts, e.g.:
Object oriented style of modelling
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)

13. Why (Description) Logic?
OWL exploits results of 15+ years of DL research
Well defined (model theoretic) semantics
Most DLs are subsets of C2, i.e., decidable fragments of FOL

14. Why (Description) Logic?
OWL exploits results of 15+ years of DL research
Well defined (model theoretic) semantics
Formal properties well understood (complexity, decidability)
I can’t find an efficient algorithm, but neither can all these famous people.
[Garey & Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.]

15. Why (Description) Logic?
OWL exploits results of 15+ years of DL research
Well defined (model theoretic) semantics
Formal properties well understood (complexity, decidability)
Known reasoning algorithms

16. Why (Description) Logic?
OWL exploits results of 15+ years of DL research
Well defined (model theoretic) semantics
Formal properties well understood (complexity, decidability)
Known reasoning algorithms
Implemented systems (highly optimised)
Pellet
KAON2
CEL

17. Class/Concept Constructors
Concept can be thought of as a FOL formula with one free variable

18. Knowledge Base / Ontology Axioms

19. OWL RDF/XML Exchange Syntax
E.g., Parent u 8hasChild.(Intelligent t Athletic):
<owl:Class>
<owl:intersectionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Parent"/>
<owl:Restriction>
<owl:onProperty rdf:resource="#hasChild"/>
<owl:allValuesFrom>
<owl:unionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Intelligent"/>
<owl:Class rdf:about="#Athletic"/>
</owl:unionOf>
</owl:allValuesFrom>
</owl:Restriction>
</owl:intersectionOf>
</owl:Class>

20. Ontology based Information Systems
Similar to relational databases
Ontology ¼ schema; instances ¼ data
Some important (dis)advantages
(Relatively) easy to maintain and update schema
Both schema and data are “self organising”
Query answers reflect both schema and data
Able to answer both intensional and extensional queries
Semantics may be counter-intuitive or even inappropriate
Open -v- closed world; axioms -v- constraints
Query answering (logical entailment) much more difficult
Can lead to scalability problems

21. Ontology based Information Systems
Very useful, but don’t expect miracles!
Similar to relational databases
Ontology ¼ schema; instances ¼ data
Some important (dis)advantages
(Relatively) easy to maintain and update schema
Both schema and data are “self organising”
Query answers reflect both schema and data
Able to answer both intensional and extensional queries
Semantics may be counter-intuitive or even inappropriate
Open -v- closed world; axioms -v- constraints
Query answering (logical entailment) much more difficult
Can lead to scalability problems

22. Ontologies and Reasoning
Development and standardisation of (Semantic) Web ontology languages has been responsible for huge increase in the development and application of ontologies.

23. Support for Ontology Engineering
Developing and maintaining quality ontolgies is very challenging
Users need tools and services, e.g., to help check if ontology is:
Meaningful — all named classes can have instances

24. Support for Ontology Engineering
Developing and maintaining quality ontolgies is very challenging
Users need tools and services, e.g., to help check if ontology is:
Meaningful — all named classes can have instances
Correct — captures intuitions of domain experts
Banana split is a kind of fruit sundae?

25. Support for Ontology Engineering
Developing and maintaining quality ontolgies is very challenging
Users need tools and services, e.g., to help check if ontology is:
Meaningful — all named classes can have instances
Correct — captures intuitions of domain experts
Minimally redundant — no unintended synonyms 
Banana split
Banana sundae

26. Support for Ontology Engineering
Range of new “non-standard” services supporting, e.g.:
Modular design and integration
What is the effect of merging O2 into O1?
In general, check that O1 [ O2 ² C iff O1 ² C for any concept C constructed using vocabulary occurring in O1
Module Extraction
Extract a (small) module from O capturing all “relevant” information about some vocabulary V
In general, find O’ µ O s.t. O’ ² C iff O ² C for any concept C constructed using terms from V
Bottom-up design
Find a (small and specific) concept describing a set of individuals
In general, find most specific C s.t. O ² C(i1) Æ … Æ C(in)
Where C may be “small” and/or in a sub-language (of O)

27. Support for Ontology Engineering
Range of new “non-standard” services supporting, e.g.:
Error diagnosis and repair

28. Support for Query Answering
In an Ontology based Information System (OIS), Query answering ¼ computing logical entailment
Reasoner needed in order to answer queries, e.g.:
C is a sub-class of D iff O ² 8 x . C(x) ! D(x)
a is an instance of C iff O ² C(a)
OIS with no reasoner ¼ DBMS with no query engine

29. Example Applications

30. e-Science
E.g., for “in silico” investigations and “hypothesis testing”
Comparing data (e.g., on proteins) to (model of) biological knowledge
Characteristics of proteins captured in an ontology O
Goal is to identify protein instances based on characteristics
Graphic showing protein functional domains (sequences of amino acids?); the identifying characteristics of different proteins.

31. e-Science
E.g., for “in silico” investigations and “hypothesis testing”
Comparing data (e.g., on proteins) to (model of) biological knowledge
Characteristics of proteins captured in an ontology O
Goal is to identify protein instances based on characteristics
Equivalent to answering queries of form:
O ² P(i)? for protein P and instance i
Result may be discovery of new kinds of protein
And these may be potential drug targets if unique to a pathenogen
Result may also be discovery of errors in model
Which may reflect gaps/errors in existing knowledge
Graphic showing protein functional domains (sequences of amino acids?); the identifying characteristics of different proteins.

32. Healthcare
UK NHS has a £6.2 billion “Connecting for Health” IT programme
Key component is Care Records Service (CRS)
“Live, interactive patient record service accessible 24/7”
Patient data distributed across local centres in 5 regional clusters, and a national DB
Detailed records held by local service providers
Diverse applications support radiology, pharmacy, etc
Applications exchange messages containing “semantically rich clinical information”
Summaries sent to national database
SNOMED-CT ontology provides common vocabulary for data
Clinical data uses terms drawn from ontology

33. SNOMED
Over 400,000 concepts

34. SNOMED Over 400,000 concepts Schema only — no instances
Language used is a (well known) fragment of OWL
NHS version extended with 1,000s of additional classes
OWL reasoner (FaCT++) used to classify and check ontology
Currently takes ¼ 4 hours
180 missing subClass relationships were found, e.g.:
Periocular_dermatitis subClassOf Disease_of_face
Fibrin_measurement subClassOf Coagulation_factor_assay

35. SNOMED Vocabulary is extensible at point of use: “post coordination”
Users (e.g. clinicians) may add/define new vocabulary
Terminology service (reasoner) used to insert in ontology
Typical new term:
almond_allergy ´ “allergy caused_by almond”
OWL reasoner (FaCT++) used to classify new term
Takes <10 ms
Classified as a kind of “nut allergy”
Clearly of crucial importance to recognise patients with allergy caused by almond as kinds of patient with nut allergy

36. Columbia Presbyterian Medical Center
Ontology used in analysis of results in path lab
OWL reasoner used to check this ontology
Several errors and omissions found that: “would have led to missed test results”

37. Recent Developments

38. Improving Scalability
Optimisation techniques
Improve performance of DL reasoners, e.g., [Tsarkov et al, JAR, 2007]
New reasoning techniques
Reduction to disjunctive Datalog [Motik et at, KR-04]
Hybrid DL-DB systems [Horrocks et al, CADE-05]
Hypertableau based algorithms [Motik et al, CADE-07]
Polynomial time algorithms for sub-ALC logics
Graph based techniques for EL+ [Baader et al, IJCAI-05]
Database techniques for DL-Lite [Calvanese et al, AAAI-05]

39. Extending Tools and Infrastructure
Editors/environments
Oiled, Protégé, Swoop, TopBraid, Ontotrack, …

40. Extending Tools and Infrastructure
Editors/environments
Oiled, Protégé, Swoop, TopBraid, Ontotrack, …
Reasoning systems
Cerebra, FaCT++, Kaon2, Pellet, Racer, CEL, …
Pellet
KAON2
CEL

41. Extending Tools and Infrastructure
Editors/environments
Oiled, Protégé, Swoop, TopBraid, Ontotrack, …
Reasoning systems
Cerebra, FaCT++, Kaon2, Pellet, Racer, CEL, …
Design methodologies
Modularity, foundational ontologies, etc.
Entity
Substantial
Quality
Event
Achievement
Stative
Accomplishment
Perdurant
Endurant

42. Increasing Expressive Power
Database style keys [Lutz et al, JAIR 2004]
Rule language extensions
W3C RIF WG (see
First order extensions (e.g., SWRL) [Horrocks et al, JWS, 2005]
Hybrid language extensions, e.g., [Eiter et al, KR-04; Motik et al, ISWC-04; Rosati, JoWS, 2005]
LP/F-Logic/Common Logic [Chen et al, JLP, 1993; de Bruijn et al, WWW-05]
Other extensions
Temporal, Fuzzy, …
OWL 1.1 extension to OWL
Clear that OWL isn’t expressive enough for all applications; users always want/need more

43. OWL 1.1 Is an extension of OWL
Addresses deficiencies identified by users and developers (at OWLED workshop)
Is based on more expressive DL: SROIQ
(OWL is based on SHOIN)
W3C working group now chartered
Will develop recommendation based on existing member submission
Already supported by popular OWL tools
Protégé, Swoop, TopBraid, FaCT++, Pellet

44. What’s New in OWL 1.1? Four kinds of features: More expressive logic
qualified cardinality restrictions, e.g.:
ObjectMinCardinality(2 friendOf hacker)
property chain inclusion axioms, e.g.:
SubObjectPropertyOf(SubObjectPropertyChain(parent brother) uncle)
local reflexivity restrictions, e.g.:
ObjectExistsSelf(likes) [for narcissists]
reflexive, irreflexive, symmetric, and antisymmetric properties, e.g.:
ReflexiveObjectProperty(knows); IrreflexiveObjectProperty(husbandOf)
disjoint properties, e.g.:
DisjointObjectProperties(childOf spouseOf)

45. What’s New in OWL 1.1? Four kinds of features:
More expressive datatypes
User-defined datatypes using facets from XML Schema Datatypes, e.g.:
SubClassOf(Adult DataSomeValuesFrom(age DatatypeRestriction(xsd:integer minInclusive "18"^^xsd:integer))
Simple relationships between values of functional data-valued properties, e.g.:
DataSomeValuesFrom(shoeSize IQ greaterThan)

46. What’s New in OWL 1.1? Four kinds of features:
Metamodelling and annotations
Names can be used as any or all of an individual, a class, or a property
Allows for a restricted form of metamodelling (“punning”), e.g.:
subClassOf(SnowLeopard BigCat)
ClassAssertion(SnowLeopard EndangeredSpecies)
Annotations of axioms as well as entities
ClassAssertion(Comment(“source: WWF”) SnowLeopard EndangeredSpecies)

47. What’s New in OWL 1.1? Four kinds of features:
Syntactic sugar (make things easier to say)
Disjoint unions, e.g.:
DisjointUnion(Element Earth Wind Fire Water)
Negative assertions, e.g.:
NegativeObjectPropertyAssertion(Ian hasChild Mary)
NegativeDataPropertyAssertion (Ian hasAge 21)

48. Tractable Fragments OWL defines only one fragment (OWL Lite)
And it isn’t very tractable!
OWL 1.1 defines several different fragments with useful computational properties
E.g., reasoning complexity in range LOGSPACE to PTIME
Smaller fragments implementable using RDBs

49. Tractable Fragments

50. Summary
Semantic Web aims to make web content more accessible to automated processes
Adds semantic annotations to web resources
OWL Ontologies provide vocabulary for annotations
Terms have well defined meaning
OWL now being used in a wide range of applications
e-Science, medicine, geography, geology, …
Reasoning enabled tools are of crucial importance
For both design and deployment of ontologies
Active research area
Expressive power, scalability, methodologies, tools, …

51. Thank you for listening

52. Thank you for listening
FRAZZ: © Jeff Mallett/Dist. by United Feature Syndicate, Inc.
Any questions?