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

Semantic WebThe Story So Far

Ian Horrocks<[email protected]>Oxford UniversityComputing Laboratory

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”

Semantic Web

• 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

Web Ontology Language OWL

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)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)

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)

[Garey & Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.]

I can’t find an efficient algorithm, but neither can all these famous people.

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)

PelletKAON2 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 onexisting 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

Any questions?

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