Ontology Mapping in Pervasive Computing Environment

1
Ontology Mapping in Pervasive Computing
Environment

• C.Y. Kong, C.L. Wang, F.C.M. Lau
• The University of Hong Kong

2
Outline

• Introduction
• Literature review
• Proposed design
• Evaluation
• Conclusion and Future works

3
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

4
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
• gt Device interoperability ?
• A new language has been developed

5
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)

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Ontology (cont)

• Example Country ontology (Source ontology)
• Example Instance

Class/Concept Properties Relationship
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Ontology Related Researches

• Context Broker Architecture (CoBrA) University
  of Maryland, 2003
• Defines a set of OWL ontologies called SOUPA
  (Standard Ontology for Ubiquitous and Pervasive
  Applications)
• Ontologies for agent, personal device, time,
  space, event, document and policy
• Enable communication between devices using
  defined ontologies
• 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

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Scenario
Smart Space B
Concepts specified by Ontology O2
Smart Space A
Proxy B
Proxy A
Resource Description --- --- --- --- --- ---
Concepts specified by Ontology O1
Request --- --- --- --- --- --- --- --- ---
Resource Description --- --- --- --- --- ---
I want to find a resource/function
Concepts specified by Ontology O3
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Scenario
Smart Space B
Concepts specified by Ontology O2
Proxy B
Concepts specified by Ontology O1
Resource Description --- --- --- --- --- ---
Request --- --- --- --- --- --- --- --- ---
Resource Description --- --- --- --- --- ---
I want to find a resource/function
Concepts specified by Ontology O3
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Ontology Mapping

• Given two ontologies O1 and O2, mapping one
  ontology onto 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
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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 medical ontology
• Matching accuracy decreases when mapping
  ontologies with more 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

12
New Challenges

• Online mapping with partial ontology information
• Efficiency
• Space limitation of devices
• Knowledge propagation

13
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

Instance
Ontology O1
Ontology O2
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Proposed Design (cont)

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

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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

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Extraction of candidate concepts

• Compare the name similarity of Ci and every
  concept C in O2
• For the k-highest name similarity concepts,
  denoted by C1..k

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Similarity of concepts Ci and Cn

• Similarity is defined as

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(U1Ci,Cn) and
  N(U1Ci,Cn)?
• (1 ) and (2) from Learning to map between
  ontologies on Semantic Web, 2002

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Find
N(U1Ci,Cn), N(U1Ci,Cn), N(U1Ci,Cn)

• N(U1Ci,Cn) means finding the number of instances
  in U1Ci that also contain Cn
• Partition U1 into two sets. One set contains
  concept Ci (denoted U1Ci) while the other set
  does not contain concept Ci (denoted U1Ci)
• Partition U2 into two sets. U2Cn and U2Cn
• N(U1Ci,Cn) N(U1Ci)StructSim(Ci,Cn)
• where StructSim(Ci,Cn) structure similarity of
  Ci and Cn
• N(U1Ci,Cn) N(U1Ci) N(U1Ci,Cn)
• N(U1Ci,Cn) N(U1Cn) N(U1Ci,Cn)
• Similarly, N(U2Ci,Cn), N(U2Ci,Cn) and N(U2Ci,Cn)

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Find
N(U1Ci,Cn), N(U1Ci,Cn), N(U1Ci,Cn)

• N(U1Ci,Cn) means finding the number of instances
  in U1Ci that also contain Cn
• Partition U1 into two sets. One set contains
  concept Ci (denoted U1Ci) while the other set
  does not contain concept Ci (denoted U1Ci)
• Partition U2 into two sets. U2Cn and U2Cn
• N(U1Ci,Cn) N(U1Ci)StructSim(Ci,Cn)
• where StructSim(Ci,Cn) structure similarity of
  Ci and Cn
• N(U1Ci,Cn) N(U1Ci) N(U1Ci,Cn)
• N(U1Ci,Cn) N(U1Cn) N(U1Ci,Cn)
• Similarly, N(U2Ci,Cn), N(U2Ci,Cn) and N(U2Ci,Cn)

20
Find
N(U1Ci,Cn), N(U1Ci,Cn), N(U1Ci,Cn)

• N(U1Ci,Cn) means finding the number of instances
  in U1Ci that also contain Cn
• Partition U1 into two sets. One set contains
  concept Ci (denoted U1Ci) while the other set
  does not contain concept Ci (denoted U1Ci)
• Partition U2 into two sets. U2Cn and U2Cn
• N(U1Ci,Cn) N(U1Ci)StructSim(Ci,Cn)
• where StructSim(Ci,Cn) structure similarity of
  Ci and Cn
• N(U1Ci,Cn) N(U1Ci) N(U1Ci,Cn)
• N(U1Ci,Cn) N(U1Cn) N(U1Ci,Cn)
• Similarly, N(U2Ci,Cn), N(U2Ci,Cn) and N(U2Ci,Cn)

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  property of Ci (denoted 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  property of Ci (denoted 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  property of Ci (denoted 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  property of Ci (denoted 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

25
Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  property of Ci (denoted 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  relationship 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  relationship 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

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Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  relationship 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

29
Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  relationship 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

30
Structure Similarity
, StructSim(Ci,Cn)

• Compute the similarity between each pair of
  relationship 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

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No. of instances
, N(U1Cn)

• 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)

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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

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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 gt90
Efficiency 6s 10s
Proposed design Instance based
Accuracy 70 70
Efficiency 6s 20s
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Conclusion

• New challenges
• Online mapping
• Efficiency
• Space limitation
• Knowledge propagation
• Partial ontology mapping
• Future work
• Experiments
• Context
• Resource instances selection

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Q A