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Context sensitivity for networked ontologies Igor Mozetič, Marko Grobelnik, Damjan Bojadžijev Jozef Stefan Institute Slovenia

Context sensitivity for networked ontologies

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Context sensitivity for networked ontologies. Igor Mozeti č , Marko Grobelnik, Damjan Bojad žijev Jozef Stefan Institute Slovenia. text _ 1. text. expl( con text). text. text. con text. explicit. implicit. global. local. I’m here. who, where. c on text _ 1. +. Overview. - PowerPoint PPT Presentation

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Context sensitivity for networked ontologies

Igor Mozetič, Marko Grobelnik, Damjan Bojadžijev

Jozef Stefan Institute

Slovenia

NeOn, Rome, 21 Mar 2006

JSI 2

text context

explicit implicit

global local

text

I’m here who, where

text expl(context)

text_1context_1

+

NeOn, Rome, 21 Mar 2006

JSI 3

Overview

Formalizing context Cyc Semantic Web C-OWL Probabilistic approaches JSI related work

NeOn, Rome, 21 Mar 2006

JSI 4

McCarthy [1993]

AI: modelling of context and use in automated reasoning

implicit -> explicit ist( context, proposition ) context = collection of assumptions

(generalization of, partially known) entering and exiting,

nesting, lifting, transcending, …

NeOn, Rome, 21 Mar 2006

JSI 5

Cyc [Lenat, Guha]

Cyc KB = set of microtheories (Mt) Microtheory = set of axioms

shared assumptions, topic internally consistent localized (more efficient) reasoning preconditions = context in which Mt is applicable

NeOn, Rome, 21 Mar 2006

JSI 6

Cyc (example)

ist( NaiveStateChangeMt, isa( ?X, Freezing ) & outputsCreated( ?X, ?Obj ) => stateOfMatter( ?Obj, SolidStateMatter ))

NaiveStateChangeMt domainAssumptions: forAll ?U isa( ?U, StateOfMatterChangeEvent ) => isa( ?U, CreationOrDestructionEvent )

NeOn, Rome, 21 Mar 2006

JSI 7

Context for Semantic Web [Guha et al]

AI SW

scope,

complexity of phenomena

scale (comp. complexity),

distributed sources,

ease of use

Aggregation from different sources. Issues:• class differences• property type differences• point of view• implicit time• approximations

NeOn, Rome, 21 Mar 2006

JSI 8

C-OWL [Giunchiglia et al]:Contextualizing ontologies

Ontologies Contexts

Global, shared model

Encode common view

Combining by import

Global semantics

Local models

Encode each party’s view

Combining by explicit mappings

Local Models Semantics

NeOn, Rome, 21 Mar 2006

JSI 9

OWL: Global semantics for multiple (networked) ontologies

sharedmodel

NeOn, Rome, 21 Mar 2006

JSI 10

OWL: Global semantics for multiple (networked) ontologies

sharedmodel

import

NeOn, Rome, 21 Mar 2006

JSI 11

C-OWL: Local model semantics

local models

NeOn, Rome, 21 Mar 2006

JSI 12

C-OWL: Mappings

contextualizedontology

context

context

NeOn, Rome, 21 Mar 2006

JSI 13

C-OWL ontology is a pair:

OWL ontology (target): concepts individuals roles

mappings (bridge rules): equivalence onto into compatible incompatible

NeOn, Rome, 21 Mar 2006

JSI 14

C-OWL example

OWL ontology (target) + mappings (bridge rules)

NeOn, Rome, 21 Mar 2006

JSI 15

C-OWL of any use?

Import ontology vs. define context mappings? (diversity as defect vs. feature)

Semantic Web = Web of Semantic links ? (context mappings)

Discovering context mappings = core issue in building Semantic Web ?

NeOn, Rome, 21 Mar 2006

JSI 16

JSI related work

Parametric temporal ontology Simultaneous ontologies User profiling Implicit document context (links)

NeOn, Rome, 21 Mar 2006

JSI 17

Temporal ontology

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now)

day(sun)day(mon) meets meets

starts finishes

NeOn, Rome, 21 Mar 2006

JSI 18

Temporal ontology

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

meets

NeOn, Rome, 21 Mar 2006

JSI 19

Temporal ontology

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

day(now)day(now-1) meets

meets

day(now+1)meets

NeOn, Rome, 21 Mar 2006

JSI 20

Temporal reasoning

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

day(now)day(now-1) meets

meets

equals

day(now+1)meets

?

NeOn, Rome, 21 Mar 2006

JSI 21

Temporal reasoning

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

day(now)day(now-1) meets

meets

equals

day(now+1)meets

day(mon)

starts

NeOn, Rome, 21 Mar 2006

JSI 22

Temporal reasoning

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

day(now)day(now-1) meets

meets

equals

day(now+1)meets

meets day(mon)

starts

NeOn, Rome, 21 Mar 2006

JSI 23

Temporal reasoning

Temporal algebra [Allen]: event = temporal interval relations: before, meets, starts, finishes, …

week(now) week(now+1)

day(sun)day(mon) meets meets

starts finishes

day(now)day(now-1) meets

meets

equals

day(now+1)meets

meets day(mon)

starts

equals

NeOn, Rome, 21 Mar 2006

JSI 24

Parameterized temporal ontology Parameters:

now order of magnitude past - future

now-1 now now+1 now+2

day

wee

k m

onth

ye

ar

deca

de

now = ?

context

NeOn, Rome, 21 Mar 2006

JSI 25

News analysis

earthquake tsunami tsunami tsunami tsunami

News stream:

the same? yet another one?

NeOn, Rome, 21 Mar 2006

JSI 26

A temporal model: Tsunami

Earthquake

Tsunami

Search & Rescue Rebuilding

~minutes

~hours

~days ~months

E T S&R

25.dec 26.dec 27.dec 28.dec 29.dec 30.dec 31.dec 1.jan 2.jan 3.jan

NeOn, Rome, 21 Mar 2006

JSI 27

News analysis: Temporal model = Context

earthquake tsunami tsunami tsunami tsunami

News stream:

model of tsunami

provides contextfor subsequentevents

NeOn, Rome, 21 Mar 2006

JSI 28

Summary

Parameterized ontology Context determines parameters

when? how long? order of magnitude

Temporal model selected by events provides context