Abstract
Ontology and rule languages for the Semantic Web are logically founded
on fragments of first-order logic, namely Description Logics (DLs) and
hybrid DL-Horn Clausal Logics. Building ontologies as well as rules on
top of ontologies is a very demanding task also from the Knowledge
Acquisition viewpoint. When performing this task, Semantic Web
practitioners could take benefit from the application of Machine
Learning (ML) methods and techniques. The ML approach known as
Inductive Logic Programming (ILP) seems to be particularly appropriate
due to the common roots with computational logic. The tutorial on Inductive Logic Programming Approaches to
Ontology and Rule
Acquisition for the Semantic Web will provide a survey of ILP
proposals for learning with DLs and hybrid DL-Horn Clausal Logics.
Attendees are expected to be knowledgeable in (quasi-)standard
languages for ontologies and rules for the Semantic Web (OWL and SWRL)
and to have basic notions of first-order logic. The tutorial will focus
mainly on theoretical aspects but the presentation will be rich in
illustrative examples, animations, etc. in order to assure that the
attendees become acquainted with the logical foundations of ontology
and rule languages and fully comprehend the potential of ILP for the
Semantic Web.
Description
Aims
The tutorial is intended to provide a survey of research in Inductive
Logic Programming restricted to the literature of interest to the
Semantic Web area, namely ILP proposals for learning with DLs and
hybrid DL-Horn Clausal Logics which can contribute to solve a hard
problem in the Semantic Web:
(semi-)automated acquisition of ontologies and rules. It covers topics
like
- Ontology Learning and Metadata Generation
- Semantic Web Rules and Query Languages
- Reasoning on the Semantic Web
- Semantic Web Mining
in equal depth.
Attendees are expected to be
knowledgeable in (quasi-)standard languages for ontologies and rules
for the Semantic Web (OWL and
SWRL) and to have
basic notions of
first-order logic.
Content
The layered architecture of the Semantic Web poses several challenges
in the field of Knowledge Representation and Reasoning (KR&R).
E.g., the design of OWL for the ontological
layer has been based on DLs, more
precisely on the DL SHIQ
(HorrocksP-SvH03). Also SWRL, recently proposed
for the logical layer,
extends OWL 'to build rules on top of ontologies'. It is intended to
bridge the notorious expressive gap between DLs and Horn clausal logic
(or its fragments) (Borgida96) in a way that is similar in the
spirit to hybridization in KR&R systems (Donini98, Rosati05).
Building ontologies as well as rules on top of ontologies is a very
demanding task also from the viewpoint of Knowledge Acquisition. When
performing this task, Semantic Web practitioners could take benefit
from the application of ML methods and techniques. The ML approach
known under the name of ILP seems to be particularly promising due to
the common roots with computational logic. ILP has
been historically concerned with concept learning from examples and
background knowledge within the representation framework of Horn
clausal logic and with the aim of prediction. More recently ILP has
moved towards either different first-order logic fragments (e.g., DLs)
or new learning goals (e.g., description). Inducing DL concept
descriptions from examples has been attacked mostly by heuristic means
(CohenBH92,CohenH94,KietzM94) and very recently in a formal manner
(BadeaNC00). Learning in hybrid DL-Horn clausal logics has very
recently attracted attention in the ILP community. In (RouveirolV2000)
the
chosen language is Carin-ALN,
therefore example
coverage and subsumption between two hypotheses are based on the
existential entailment algorithm of Carin. Following (RouveirolV2000),
Kietz studies the learnability of Carin-ALN, thus providing a
pre-processing method
which enables ILP systems to learn Carin-ALN rules (Kietz03). In
(Lisi05), Lisi proposes a general framework for
learning in AL-log.
Presentation style
The tutorial will focus mainly on theoretical aspects but the
presentation will be rich in illustrative examples, animations, etc. in
order to assure that the attendees become acquainted with the logical
foundations of ontology and rule languages and fully comprehend the
potential of ILP for the Semantic Web.
Schedule
First part: Logical
Foundations of Ontology and Rule Languages for the Semantic Web
(1h)
- Description Logics (DLs) (BaaderCMcGNPS03)
- Hybrid DL-Horn Clausal Logics (e.g.,
(Borgida96,Donini98,Rosati05))
Second part: Inductive Logic
Programming with DL-based representations (2h)
- Introduction to Inductive Logic Programming (ILP) (Nienhuys97)
- ILP with DLs (e.g., (CohenBH92,CohenH94,KietzM94,BadeaNC00))
- ILP with hybrid DL-Horn Clausal Logics (e.g.,
(RouveirolV2000,Kietz03,Lisi05))
Bibliography
- (BaaderCMcGNPS03)
- (BadeaNC00)
- (Borgida96)
- (CohenBH92)
- (CohenH94)
- (Donini98)
- (HorrocksP-SvH03)
- (Kietz03)
- (KietzM94)
- (Lisi05)
- (Nienhuys97)
- (Rosati05)
- (RouveirolV2000)