Transcript Semantic Web: the Story So Far

Semantic Web
The Story So Far
Ian Horrocks
<[email protected]>
Oxford University
Computing Laboratory
Semantic Web
Semantic Web
• According to W3C
“an evolving extension of the World Wide Web in which web
content can be … read and used by software agents, thus
permitting them to find, share and integrate information more
easily”
• Data will use uniform syntactic structure (RDF)
• (OWL) ontologies will provide
– Schemas for data
– Vocabulary for annotations
• Ultimate goal is a “more intelligent web”
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
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 (AKA classes), roles
(AKA properties, relationships) and individuals
– Operators allow for composition of complex concepts
– Names can be given to complex concepts, e.g.:
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
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
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.]
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
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)
KAON2
Pellet
CEL
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
• Schema plus data are integrated in a logical theory
+ 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
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
Useful, but not miraculous!
Ontologies and Reasoning
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
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
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
Support for Query Answering
• In an Ontology Based Information System (OBIS),
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)
OBIS with no reasoner ¼ DBMS with no query engine
Recent Developments
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
Tool Support for Modular Design
• Check when integration of modules is “safe”
– Interface between modules via exported vocabulary
– Information flows from imported to importing ontology
– No information flows back the other way
• Extract smaller modules from large ontologies
– E.g., starting with SNOMED, extract module for “Heart”
– Tool should ensure that module
• Is small (and preferably minimal), but
• Still contains all “relevant knowledge”
[Cuenca Grau & Kazakov, IJCAI-07 & WWW-07]
Extending Expressive Power
• Database style keys [Lutz et al, JAIR 2004]
– E.g., make + model + chassis-number is a key for Vehicles
• Rule language extensions
– W3C RIF WG (see http://www.w3.org/2005/rules/)
– 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
– Extended annotation framework
– Macro language
– …
Extended Query Language
• Standard reasoning techniques only provide for simple queries
– E.g., return all instances of a (possibly complex) concept C
• Practical applications may need a richer query language
– E.g., retrieve tuples (?x, ?y, ?z), where:
• ?x is an R5 Phosphatase,
• ?x contains the phosphatase domains (p-domains) ?y and ?z,
• ?y is a Catalytic domain, and ?z is a Fibronectin domain
Improving Scalability
• Optimisation techniques
– Improve performance of DL reasoners, e.g., [Tsarkov, Horrocks et al, JAR, 2007]
• New Reasoning Techniques
– Reduction to disjunctive Datalog
[Motik et at, KR-04]
• Transform SHOIN ontology into DatalogÇ program
• Use LP techniques to deal with large numbers of ground facts
– Hybrid DL-DB systems
[Horrocks et al, CADE-05]
• Use DB to store “Abox” (individual) axioms
• Cache inferences and use DB queries to answer/scope logical queries
– Hypertableau based algorithms [Motik et al, CADE-07]
• Prototypical implementation in HermiT system
• 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]
Thank you for listening
Thank you for listening
FRAZZ:
© Jeff Mallett/Dist. by United Feature Syndicate, Inc.
Any questions?