Semantic Web: the Story So Far

boorishadamantAI and Robotics

Oct 29, 2013 (3 years and 9 months ago)

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Semantic Web

The Story So Far

Ian Horrocks

<ian.horrocks@comlab.ox.ac.uk>

Oxford University

Computing 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

8
hasChild.(Intelligent
t
Athletic)

HappyParent

´

Parent
u

8
hasChild.(
Intelligent
t

Athletic
)

HappyParent

´

Parent
u

8
hasChild
.(Intelligent
t
Athletic)

HappyParent

´

Parent

u

8
hasChild.(Intelligent

t
Athletic)

HappyParent

´

Parent
u

8
hasChild.(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)

Pellet

KAON2

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 Datalog
Ç

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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©
Jeff Mallett/Dist. by United Feature Syndicate, Inc.