Context Representation and

wrendeceitInternet και Εφαρμογές Web

21 Οκτ 2013 (πριν από 3 χρόνια και 8 μήνες)

62 εμφανίσεις

Context Representation and
Reasoning with Formal
Ontologies

Juan Gómez
-
Romero
1,2
, University Carlos III of Madrid (Spain)

Fernando Bobillo
2
, University of Zaragoza (Spain)

Miguel Delgado
2
, University of Granada (Spain)


Activity Context Workshop
, AAAI’11, August, 2011

(1)
Applied Artificial Intelligence Group

(2)
Approximate Reasoning and A.I. Group



Modeling context knowledge with
ontologies

Context representation

Represent context information with standard
ontologies


Context
-
based reasoning

Reduce the knowledge search space according to current context

Extensions to non
-
classical
ontologies

Representation of vague, imprecise and uncertain knowledge




Representation of
context knowledge

to reason what is


significant

and

summarize

available knowledge




Context representation and reasoning with formal
ontologies

2

Aug, 7th 2011



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

3



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

4

1. An unified view on context

Schmidt,
Beigl

and
Gellersen

(1999):

Mix of geo
-
spatial data, ambient sensor inputs, user profiles (preferences,
intentions, history, etc.), and service descriptions

Dey

and
Abowd

(2001):

Any information (either implicit or explicit) that can be used to characterize the
situation of an entity

Henricksen

(2003):

The context of a task is the set of circumstances surrounding it that are
potentially of relevance to its completion

Kandefer

and Shapiro (2008)
:

The structured set of
variable, external constraints

to some (natural or artificial)
cognitive process
that
influences the behavior
of that process in the agent(s)
under consideration

Gomez
-
Romero et al. (2011):

Any information of interest to the application not directly obtained by the domain
data acquisition sensors: common
-
sense, human feedback, external or a priori
resources, etc.



Definitions

Aug, 7th 2011

Context representation and reasoning with formal ontologies

5

1. An unified view on context


Set of
constraints

to a reasoning process

Soft:
Delimit relevant information

Hard:
Check consistency of world interpretation


Influence
behavior

of the
agent

Adapt system functioning to the environment

Avoid information overload

Augment or embellish system results

Modify acquired data and acquisition procedures


Cognitive

process

Use of formal specifications vs. ad hoc specifications

Context is “first
-
level” knowledge


Characteristics

Aug, 7th 2011

Context representation and reasoning with formal ontologies

6

1. An unified view on context

Nomadic Access to Healthcare Information

A physicist wants to prescribe a treatment for a patient

The HIS provide a report of the patient’s clinical history


Information overload
: Include only information relevant to the
patient’s state, the diagnosis, and clinical procedure that is being carried out

Patient is unconscious and has a hemorrhagic laceration

Allergies to procaine should be taken into account


The example can be extended to other Semantic Web scenarios

Keyword
-
indexed documents

Query expansion, query restriction

Data visualization

http://ecolexicon.ugr.es/visual/index_en.html

(Java required)

Example case

Aug, 7th 2011

Context representation and reasoning with formal ontologies

7



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

8

2. Ontologies for context representation

Representation of the
mereological

aspects of a reality
created from a common perspective and expressed in a
formal language

Representation formalism that promotes knowledge
integration, sharing and reuse


Based on Description Logics (DLs), a family of logics with well
-
defined
semantics specially designed to represent structured knowledge

DLs are classified in levels (and named) according to their expressivity,
which determines the computational complexity of reasoning with the
logic (in general DLs,
NE
XPTIME
-
COMPLETE
)

The Semantic Web uses
ontologies

to represent metadata and offers
several supporting tools, such as the standard OWL language

Ontologies

Aug, 7th 2011

Context representation and reasoning with formal ontologies

9

2. Ontologies for context representation

Concepts

(classes, types)

Set of objects with common features

FOL unary predicates

Instances

(individuals)

Objects belonging to a class

FOL constants

Relations

(properties, roles)

Binary associations between two instances or an instance and a data
type value (integers, strings, etc.)

FOL binary predicates

Axioms

Restrictions defining concept, instance and relation features

FOL formulas

Elements

Aug, 7th 2011

Context representation and reasoning with formal ontologies

10

2. Ontologies for context representation

Elements


Aug, 7th 2011

Context representation and reasoning with formal ontologies

11

Context vocabulary

Context description



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

12

3. Reasoning with context ontologies

Automatic procedure to obtain
implicit
axioms from
explicit

axioms

modus ponens

A

A


B

B


Tableaux

algorithms

Reasoning algorithms for DLs

Implemented by inference engines (HermiT, RACER, Pellet)

Theoretical efficiency is high, but worst cases are not frequent


Ontology reasoning

Aug, 7th 2011

Context representation and reasoning with formal ontologies

13

Resolution

(
propositional

logic
)




3. Reasoning with context ontologies

Concept axioms

Satisfiability

/ Consistency

A concept is
satisfiable

if it is not a contradiction to the remaining axioms

Subsumption

A (super
-
)concept includes a (sub
-
)concept

Equivalence


Two concepts include the same instances

Disjointness

Two concepts do not have any common instance


Instance axioms

Satisfiability

/ Consistency

An instance assertion is
satisfiable

if it is not a contradiction to the remaining axioms

Instance checking

An instance belongs to a class


Entailment

An axiom is a logical consequence of a set of axioms

Standard reasoning tasks

Aug, 7th 2011

Context representation and reasoning with formal ontologies

14

3. Reasoning with context ontologies

Context representation and reasoning

Exploitation of
ontologies

in context
-
aware ubiquitous computing

Interpreting the current user situation

Using contextual knowledge to improve the performance of the system

Contextualization of
ontologies

How external or additional
knowledge influences the interpretation
of
an ontology: consistency, validity, partitioning

Non
-
monotonic models
vs

monotonic DLs

Extend the OWL language with non
-
monotonic features

Ontology design patterns

Recipes

to help ontology developers to capture aspects of the application
domain and
represent them with existing languages
from a common
and well
-
understood perspective

No specific pattern aimed to the representation of context knowledge, either for
specific or general domains



Dealing with context in
ontologies

Aug, 7th 2011

Context representation and reasoning with formal ontologies

15

3. Reasoning with context ontologies

Proposal

Meta
-
model: design pattern to create context
-
aware
ontologies

that avoid information overload.


Significance
ontologies

to represent which
information of
the domain

is
relevant in a given context


CDS (Context
-
Domain Significance) pattern formulated in the
basic DL ALC

Directly translatable into OWL (≈ SHOIN(D))


In several cases, fuzzy knowledge must be considered

Extension of the pattern using fuzzy DLs

CDS pattern


Aug, 7th 2011

Context representation and reasoning with formal ontologies

16

3. Reasoning with context ontologies

Base
ontologies

Context ontology

(
K
C
)
: vocabulary to describe context situations.

Domain ontology

(
K
D
)
: ontology to represent domain
-
specific
knowledge.

New significance ontology
:

CDS ontology
(
K
S
)

Complex contexts (
C
i

):

Concepts created using terms of
K
C
.

Complex domains (
D
j

):

Concepts created using terms of
K
D
.


s
-
connection

(
s
i,j

or
P
i,j
):

A concept
linking

a complex context
C
i

and a complex domain
D
j

Denotes that
D
j

is significant in situation
C
i

CDS pattern


Aug, 7th 2011

Context representation and reasoning with formal ontologies

17

3. Reasoning with context ontologies

18

Aug, 7th 2011

Context representation and reasoning with formal ontologies

Domain

ontology

Context

ontology

3. Reasoning with context ontologies

Reasoning with the CDS pattern


Aug, 7th 2011

Context representation and reasoning with formal ontologies

19

Domain knowledge

I

significant

in a scenario

E


Algorithm
(implemented in the CDS API)
:

1.
Retrieve the complex contexts
C
n

more general than
E

2.
Retrieve

the

s
-
connections

P
n,m

involving

C
n

3.
Retrieve the complex domains
D
m

involved in
P
n,m

4.
Retrieve the concepts
I

of the domain more specific than
D
m


Complete and decidable

Complexity

is determined by
C
i

and

D
j

(
E
XP
T
IME
-
complete
for
ALC
)



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

20

4. Extending ontologies to the fuzzy case

Imprecise

knowledge
cannot be represented

E.g.:
A patient is slightly unconscious

Partial similarities
between contexts
cannot be represented

E.g.:
Anaphylaxis is quite similar to sepsis

Relevance relations
cannot hold to a degree

E.g.:
Blood
-
borne diseases are less relevant than drug intolerances


Fuzzy extension of the crisp meta
-
model
, i.e. a
design pattern

to
create
fuzzy

context
-
aware ontologies that avoid information
overload and
allow vague knowledge
to be managed


Limitations of CDS to manage context knowledge

Aug, 7th 2011

Context representation and reasoning with formal ontologies

21

4. Extending ontologies to the fuzzy case

The significance ontology is a
fuzzy ontology
(
fCDS
) created with
an adaptation of the crisp rules of the CDS pattern


The fuzzy significance ontology is expressed with the
fuzzy
Description Logic f
ALC

Fuzzy DLs extends DLs to the fuzzy case



Concepts are fuzzy sets



Axioms hold to a degree
(inclusion!)



Roles are fuzzy relations



Interpretation has fuzzy semantics


Reasoning can be performed with a fuzzy DL reasoner or by
reducing the fuzzy ontology to an equivalent crisp DL ontology

and using a crisp inference engine (Bobillo, Delgado & Gómez
-
Romero, 2009)


Fuzzy CDS pattern

Aug, 7th 2011

Context representation and reasoning with formal ontologies

22

4. Extending ontologies to the fuzzy case

23

Aug, 7th 2011

Context representation and reasoning with formal ontologies

4. Extending ontologies to the fuzzy case

Reasoning with the fuzzy CDS pattern


24

Domain knowledge

I
a
-
significant

in a scenario

E

Knowledge significant and
degree

of significance

aggregation: min t
-
norm
a




greatest lower bound: glb =
sup
{
a

:
K


<
t

a
>}

Complete and decidable

Complexity

is determined by
C
i
, D
j
, and the
glbs

to be
calculated



Outline

1.
A unified view of context (?)

2.
Ontologies

for context representation

3.
Reasoning with context
ontologies

4.
Extending
ontologies

to the fuzzy case

5.
Conclusions and future work



Aug, 7th 2011

Context representation and reasoning with formal ontologies

25

5. Conclusions and future work

Advantages
of using
ontologies

to manage context knowledge

Expressiveness

Formal representation and reasoning

Standard languages and tools

Appropriate to deal with information overload

Extensions are being studied


Future

research

Standard specification of
common context dimensions
: location,
time, preferences, etc.

Privacy

issues

Study the applicability of
full
-
fledged reasoning

in real
-
world
applications

Relation with
context acquisition and interpretation

techniques

Are
fuzzy extensions necessary/convenient
?


Notice!

Aug, 7th 2011

Context representation and reasoning with formal ontologies

26










Thank you!





Questions, comments?





Aug, 7th 2011

Context representation and reasoning with formal ontologies

27