ontology mapping in pervasive computing environment

Ontology Mapping in Pervasive Computing Environment

C.Y. Kong, C.L. Wang, F.C.M. Lau

The University of Hong Kong

Outline

Introduction Literature review Proposed design Evaluation Conclusion and Future works

Pervasive Computing M. Satyanarayanan - An environment saturated with computing

and communication capability, yet so gracefully integrated with users that it becomes a “technology that disappears”.

Various information flows: User intent Resource discovery and query Context information

Different types of computers communicate Smart devices Surrogates Sensors Peer-to-peer communication

Infeasible to expect all computers to have the same semantics on different information. i.e. the vocabulary of the messages, which includes the name and valid values of message elements

XML A language commonly used for data exchange XML sets rules for syntax for structured documents but it

does not provide meanings for terms Same term may be used with different meaning in different

context Different term may be used for items that have the same

meaning Hence, human needs to be involved to agree on tag

names or mappings between roughly equivalent sets of tags in related standard=> Device interoperability ↓

A new language has been developed

Ontology Provide a formal, explicit specification of a shared

conceptualization of a domain that can be communicated between people and heterogeneous and widely spread application systems

A formal explicit description of concepts in a domain of discourse (classes), properties of each concept describing various features and attributes of the concept (slot) and restrictions on these properties

Provide meanings for terms when information exchange Bridge knowledge gaps between different domains Enable knowledge sharing in open and dynamic distributed

systems Allow devices and agents not expressly designed to work

together to interoperate (i.e. better device interoperability)

Ontology (cont) Example: Country ontology (Source ontology)

Example: Instance

Country

name

City

located_in

capital

Geographical Location

name

Land Boundary

neighbor_countrypart_of

has_boundary

Country

Japan

City

Tokyo

capital

Geographical Location

Asia

Land Boundary

Koreapart_of

has_boundary

Class/Concept

Properties

Relationship

Ontology Related Researches Context Broker Architecture (CoBrA) [University of Maryl

and, 2003] Defines a set of OWL ontologies called SOUPA (Standard Ontol

ogy for Ubiquitous and Pervasive Applications) Ontologies for agent, personal device, time, space, event, docu

ment and policy Enable communication between devices using defined ontologie

s GAIA [University of Illinois, 2002]

Defines a set of ontologies for active space such as entity and context information

Enable communication between devices using defined ontologies

Communications may involve terms from different ontologies

Hence, Ontology Mapping is introduced

Scenario

I want to find a resource/function

Proxy A

Request

--- --- ---

--- --- ---

--- --- ---

Concepts specified

by Ontology O1

Resource Description

--- --- ---

--- --- ---

Concepts specified by Ontology O2


--- --- ---

--- --- ---


Proxy B

Smart Space B

Smart Space A

Scenario

I want to find a resource/function

Request

--- --- ---

--- --- ---

--- --- ---

Concepts specified

by Ontology O1


--- --- ---

--- --- ---



--- --- ---

--- --- ---


Proxy B

Smart Space B

Ontology Mapping Given two ontologies O1 and O2, mapping one ontology o

nto another means that for each entity (concept, relation or instance) in ontology O1, we try to find a corresponding entity, which has the same intended meaning, in ontology O2

Ontology O1 Ontology O2

Literature Review Source-based

Mappings are done by comparing the similarity of the concepts based on the properties of the concepts and the structure of the ontology defined in the source ontologies

Example: PROMPT [Stanford, 2000] Work well for ontologies having a specialized terminology like m

edical ontology Matching accuracy decreases when mapping ontologies with mo

re general terminologies Instance-based

Mappings are done by comparing the similarity of the concepts based on the source ontologies and their instances

Example: FCA-Merge [University of Karlsruhe ,2001], GLUE [University of Illinois and University of Washington, 2002]

Structure and properties of the concepts are not taken into consideration

Accuracy varies based on the instance sets

New Challenges

Online mapping with partial ontology information

Efficiency Space limitation of devices Knowledge propagation

Proposed Design Partial Ontology Mapping

A device submits a resource or function request (an instance I1 of ontology O1)

A resource or function is present and described by O2

Map all the concepts used in I1 with the concepts in O2 Number of concepts to be mapped reduces More efficient

Ontology O1 Ontology O2

Instance

Proposed Design (cont)

Hybrid of source-based and instance-based ontology mapping Properties of the concept and its relationships with oth

er concepts are considered Instances are considered to increase accuracy Store evaluation results of instances to

avoid handling large number of instances at one time but, still provide a reasonable amount of instances for mappi

ng

Methodology Notation

O1: source ontology of the request instance O2: source ontology of the resource Ir: request instance

For each concept (Ci) appear in Ir, Find a set of candidate concepts in O2

For each candidate concepts (Cn) Compute the similarity of Ci and Cn

The candidate concept with maximum similarity degree is the mapped concept of Ci

History Records

Extraction of candidate concepts Compare the name similarity of Ci and every concept C’ i

n O2

For the k-highest name similarity concepts, denoted by C1..k

Ci namelen

bstringlongest sulen ' nameCi name, CSim

..kof C concepts ub-class)chidren (s..kof C concepts er class)parent (

ighbor of its ne with each..kcepts of Cmerged con..k with Clationshiphat has reconcepts t

..kC

et andidate sPossible c

1

1sup1

1

1

Similarity of concepts Ci and Cn Similarity is defined as

ninini

ni

ni

ni

CCPCCPCCP

CCP

CCP

CCP

,~~,,

,

(1)

21

,

2

,

1,UNUN

UNUNCCP

CnCiCnCi

ni

(2)

where

Ux: instance set of ontology Ox

UxCi,Cn: instance set of ontology Ox that contains concepts Ci and Cn

N(instance set): Number of instances in the instance set

How to find N(U1Ci,Cn), N(U

1Ci,~Cn) and N(U

1~Ci,Cn)?

(1 ) and (2) from “Learning to map between ontologies on Semantic Web”, 2002

Find N(U1

Ci,Cn) means finding the number of instances in U1

Ci that also contain Cn

Partition U1 into two sets. One set contains concept Ci (denoted U1

Ci) while the other set does not contain concept Ci (denoted U1

~Ci) Partition U2 into two sets. U2

Cn and U2~Cn

N(U1Ci,Cn) = N(U1

Ci)*StructSim(Ci,Cn)

where StructSim(Ci,Cn): structure similarity of Ci and Cn

N(U1Ci,~Cn) = N(U1

Ci) – N(U1Ci,Cn)

N(U1~Ci,Cn) = N(U1

Cn) – N(U1Ci,Cn)

Similarly, N(U2Ci,Cn), N(U2

Ci,~Cn) and N(U2~Ci,Cn)

N(U1Ci,Cn), N(U1

Ci,~Cn), N(U1~Ci,Cn)

Find N(U1

Ci,Cn) means finding the number of instances in U1

Ci that also contain Cn

Partition U1 into two sets. One set contains concept Ci (denoted U1

Ci) while the other set does not contain concept Ci (denoted U1

~Ci) Partition U2 into two sets. U2

Cn and U2~Cn

N(U1Ci,Cn) = N(U1

Ci)*StructSim(Ci,Cn)where StructSim(Ci,Cn): structure similarity of Ci and Cn

N(U1Ci,~Cn) = N(U1

Ci) – N(U1Ci,Cn)

N(U1~Ci,Cn) = N(U1

Cn) – N(U1Ci,Cn)

Similarly, N(U2Ci,Cn), N(U2

Ci,~Cn) and N(U2~Ci,Cn)

N(U1Ci,Cn), N(U1

Ci,~Cn), N(U1~Ci,Cn)

Structure Similarity Compute the similarity between each pair of property of Ci (deno

ted by PCi) and property of Cn (dentoed by PCn)

Instance Similarity is the similarity of the content of the instances

Property Similarity

for x = 1 to number of properties of Cn

, StructSim(Ci,C

n)

tyceSimilari*Inswdatatype)Pdatatype, Sim(P* w

ycardinality,PcardinalitP*Simwnamename,PP*Simw

,PPSim

CnCi

CnCiCnCi

CnCi

tan43

21

21 tantan

2tan1tan

ce of Onsproperty iNce of Onsproperty iN

bstringlongest suN

ceof ins, content ce inscontent ofSim

)),((

Pr

CnCin PP * Simty x in Cof properifrequency average

ilarityoperty sim

Structure Similarity, StructSim(Ci,C

n) Compute the similarity between each pair of relationsh

ip of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)

Relationship Similarity

for x = 1 to number of relationships of Cn

Structure Similarity

typeRtypeR*Simw

ycardinalitRycardinalitR*SimwnameRnameR*Simw

RRSim

CnCi

CnCiCnCi

CnCi

,

),(,

,

3

21

)( ni and CCn concept onship x i of relatisimilarityaverage

rityhip simila*relationswsimilarity*property w 21

No. of instances

Estimate the similarity between ontology O1 and O2

where N(O1) and N(O2) are the number of concepts in O1 and O2

N(U1Cn)

, N(U1Cn)

21

21

,

ONON

conceptslar nameer of simitotal numbOOSim

2

221 ),( CU*NOOSim

History Records Caching mapping results

Increase efficiency Caching instance mapping results

Maintain a reasonable amount of instances for mapping

Increase accuracy and reduce space usage Popularity counters

Each property or relationship of a concept has a popularity counter

Act as a weight for the importance of the concept Increase accuracy and reduce space usage Knowledge accumulation

Knowledge propagation

Evaluation Programming language: Java 1.4.2 Ontology language: OWL (Ontology Web Language) Ontology Parser: Jena 2.1 Input source ontologies:

Semantic Web Research Community (SWRC) ontology: 24 concepts

Manually created a similar concept as SWRC ontology: 20 concepts

Request instance: 6 – 8 concepts Result

Proposed design

Source based

Accuracy 80% >90%

Efficiency 6s 10s

Proposed design

Instance based

Accuracy ~70% ~70%

Efficiency 6s 20s

Conclusion New challenges

Online mappingEfficiencySpace limitationKnowledge propagation

Partial ontology mapping Future work

ExperimentsContextResource instances selection

ontology mapping in pervasive computing environment

Documents

soupa standard ontology

different information

active space

different ontologieshence

different meaning

related standard

personal device

policyenable communication