Ontology Mapping in Pervasive Computing Environment

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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. - PowerPoint PPT Presentation

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<ul><li><p>Ontology Mapping in Pervasive Computing EnvironmentC.Y. Kong, C.L. Wang, F.C.M. LauThe University of Hong Kong</p></li><li><p>OutlineIntroductionLiterature reviewProposed designEvaluationConclusion and Future works</p></li><li><p>Pervasive ComputingM. 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 intentResource discovery and queryContext informationDifferent types of computers communicateSmart devicesSurrogatesSensorsPeer-to-peer communicationInfeasible 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</p></li><li><p>XMLA language commonly used for data exchangeXML sets rules for syntax for structured documents but it does not provide meanings for termsSame term may be used with different meaning in different contextDifferent term may be used for items that have the same meaningHence, 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</p></li><li><p>OntologyProvide a formal, explicit specification of a shared conceptualization of a domain that can be communicated between people and heterogeneous and widely spread application systemsA 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 propertiesProvide meanings for terms when information exchangeBridge knowledge gaps between different domainsEnable knowledge sharing in open and dynamic distributed systemsAllow devices and agents not expressly designed to work together to interoperate (i.e. better device interoperability)</p></li><li><p>Ontology (cont)Example: Country ontology (Source ontology)</p><p>Example: InstanceClass/ConceptPropertiesRelationship</p></li><li><p>Ontology Related ResearchesContext 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 policyEnable communication between devices using defined ontologiesGAIA [University of Illinois, 2002]Defines a set of ontologies for active space such as entity and context informationEnable communication between devices using defined ontologiesCommunications may involve terms from different ontologiesHence, Ontology Mapping is introduced</p></li><li><p>ScenarioI want to find a resource/functionProxy ARequest--- --- ------ --- ------ --- ---Concepts specifiedby Ontology O1Resource Description--- --- ------ --- ---Concepts specified by Ontology O2Resource Description--- --- ------ --- ---Concepts specified by Ontology O3Proxy BSmart Space BSmart Space A</p></li><li><p>ScenarioI want to find a resource/functionRequest--- --- ------ --- ------ --- ---Concepts specifiedby Ontology O1Resource Description--- --- ------ --- ---Concepts specified by Ontology O2Resource Description--- --- ------ --- ---Concepts specified by Ontology O3Proxy BSmart Space B</p></li><li><p>Ontology MappingGiven 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 O2Ontology O1Ontology O2</p></li><li><p>Literature ReviewSource-basedMappings 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 ontologyMatching accuracy decreases when mapping ontologies with more general terminologiesInstance-based Mappings are done by comparing the similarity of the concepts based on the source ontologies and their instancesExample: 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 considerationAccuracy varies based on the instance sets</p></li><li><p>New ChallengesOnline mapping with partial ontology informationEfficiencySpace limitation of devicesKnowledge propagation</p></li><li><p>Proposed DesignPartial Ontology MappingA device submits a resource or function request (an instance I1 of ontology O1)A resource or function is present and described by O2Map all the concepts used in I1 with the concepts in O2Number of concepts to be mapped reducesMore efficientOntology O1Ontology O2Instance</p></li><li><p>Proposed Design (cont)Hybrid of source-based and instance-based ontology mappingProperties of the concept and its relationships with other concepts are consideredInstances are considered to increase accuracyStore evaluation results of instances to avoid handling large number of instances at one timebut, still provide a reasonable amount of instances for mapping</p></li><li><p>MethodologyNotationO1: source ontology of the request instanceO2: source ontology of the resourceIr: request instanceFor each concept (Ci) appear in Ir,Find a set of candidate concepts in O2For each candidate concepts (Cn) Compute the similarity of Ci and CnThe candidate concept with maximum similarity degree is the mapped concept of CiHistory Records</p></li><li><p>Extraction of candidate conceptsCompare the name similarity of Ci and every concept C in O2</p><p>For the k-highest name similarity concepts, denoted by C1..k</p></li><li><p>Similarity of concepts Ci and CnSimilarity is defined aswhere Ux: instance set of ontology OxUxCi,Cn: instance set of ontology Ox that contains concepts Ci and Cn N(instance set): Number of instances in the instance setHow to find N(U1Ci,Cn), N(U1Ci,~Cn) and N(U1~Ci,Cn)?</p><p>(1 ) and (2) from Learning to map between ontologies on Semantic Web, 2002</p></li><li><p>FindN(U1Ci,Cn) means finding the number of instances in U1Ci that also contain CnPartition U1 into two sets. One set contains concept Ci (denoted U1Ci) while the other set does not contain concept Ci (denoted U1~Ci)Partition U2 into two sets. U2Cn and U2~CnN(U1Ci,Cn) = N(U1Ci)*StructSim(Ci,Cn)where StructSim(Ci,Cn): structure similarity of Ci and CnN(U1Ci,~Cn) = N(U1Ci) N(U1Ci,Cn) N(U1~Ci,Cn) = N(U1Cn) N(U1Ci,Cn) Similarly, N(U2Ci,Cn), N(U2Ci,~Cn) and N(U2~Ci,Cn)N(U1Ci,Cn), N(U1Ci,~Cn), N(U1~Ci,Cn)</p></li><li><p>FindN(U1Ci,Cn) means finding the number of instances in U1Ci that also contain CnPartition U1 into two sets. One set contains concept Ci (denoted U1Ci) while the other set does not contain concept Ci (denoted U1~Ci)Partition U2 into two sets. U2Cn and U2~CnN(U1Ci,Cn) = N(U1Ci)*StructSim(Ci,Cn)where StructSim(Ci,Cn): structure similarity of Ci and CnN(U1Ci,~Cn) = N(U1Ci) N(U1Ci,Cn) N(U1~Ci,Cn) = N(U1Cn) N(U1Ci,Cn) Similarly, N(U2Ci,Cn), N(U2Ci,~Cn) and N(U2~Ci,Cn)N(U1Ci,Cn), N(U1Ci,~Cn), N(U1~Ci,Cn)</p></li><li><p>FindN(U1Ci,Cn) means finding the number of instances in U1Ci that also contain CnPartition U1 into two sets. One set contains concept Ci (denoted U1Ci) while the other set does not contain concept Ci (denoted U1~Ci)Partition U2 into two sets. U2Cn and U2~CnN(U1Ci,Cn) = N(U1Ci)*StructSim(Ci,Cn)where StructSim(Ci,Cn): structure similarity of Ci and CnN(U1Ci,~Cn) = N(U1Ci) N(U1Ci,Cn) N(U1~Ci,Cn) = N(U1Cn) N(U1Ci,Cn) Similarly, N(U2Ci,Cn), N(U2Ci,~Cn) and N(U2~Ci,Cn)N(U1Ci,Cn), N(U1Ci,~Cn), N(U1~Ci,Cn)</p></li><li><p>Structure Similarity</p><p>Compute the similarity between each pair of property of Ci (denoted by PCi) and property of Cn (dentoed by PCn)</p><p>Instance Similarity is the similarity of the content of the instances</p><p>Property Similarity</p><p>for x = 1 to number of properties of Cn, StructSim(Ci,Cn)</p></li><li><p>Structure Similarity</p><p>Compute the similarity between each pair of property of Ci (denoted by PCi) and property of Cn (dentoed by PCn)</p><p>Instance Similarity is the similarity of the content of the instances</p><p>Property Similarity</p><p>for x = 1 to number of properties of Cn, StructSim(Ci,Cn)</p></li><li><p>Structure Similarity</p><p>Compute the similarity between each pair of property of Ci (denoted by PCi) and property of Cn (dentoed by PCn)</p><p>Instance Similarity is the similarity of the content of the instances</p><p>Property Similarity</p><p>for x = 1 to number of properties of Cn, StructSim(Ci,Cn)</p></li><li><p>Structure Similarity</p><p>Compute the similarity between each pair of property of Ci (denoted by PCi) and property of Cn (dentoed by PCn)</p><p>Instance Similarity is the similarity of the content of the instances</p><p>Property Similarity</p><p>for x = 1 to number of properties of Cn, StructSim(Ci,Cn)</p></li><li><p>Structure Similarity</p><p>Compute the similarity between each pair of property of Ci (denoted by PCi) and property of Cn (dentoed by PCn)</p><p>Instance Similarity is the similarity of the content of the instances</p><p>Property Similarity</p><p>for x = 1 to number of properties of Cn, StructSim(Ci,Cn)</p></li><li><p>Compute the similarity between each pair of relationship of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)</p><p>Relationship Similarity</p><p>for x = 1 to number of relationships of Cn</p><p>Structure SimilarityStructure Similarity</p><p>, StructSim(Ci,Cn)</p></li><li><p>Structure Similarity</p><p>, StructSim(Ci,Cn)Compute the similarity between each pair of relationship of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)</p><p>Relationship Similarity</p><p>for x = 1 to number of relationships of Cn</p><p>Structure Similarity</p></li><li><p>Structure Similarity</p><p>, StructSim(Ci,Cn)Compute the similarity between each pair of relationship of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)</p><p>Relationship Similarity</p><p>for x = 1 to number of relationships of Cn</p><p>Structure Similarity</p></li><li><p>Structure Similarity</p><p>, StructSim(Ci,Cn)Compute the similarity between each pair of relationship of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)</p><p>Relationship Similarity</p><p>for x = 1 to number of relationships of Cn</p><p>Structure Similarity</p></li><li><p>Structure Similarity</p><p>, StructSim(Ci,Cn)Compute the similarity between each pair of relationship of Ci (denoted by RCi) and relationship of Cn (dentoed by RCn)</p><p>Relationship Similarity</p><p>for x = 1 to number of relationships of Cn</p><p>Structure Similarity</p></li><li><p>No. of instancesEstimate the similarity between ontology O1 and O2where N(O1) and N(O2) are the number of concepts in O1 and O2</p><p>N(U1Cn)</p><p>, N(U1Cn)</p></li><li><p>History RecordsCaching mapping resultsIncrease efficiencyCaching instance mapping resultsMaintain a reasonable amount of instances for mappingIncrease accuracy and reduce space usagePopularity countersEach property or relationship of a concept has a popularity counterAct as a weight for the importance of the conceptIncrease accuracy and reduce space usageKnowledge accumulationKnowledge propagation</p></li><li><p>EvaluationProgramming language: Java 1.4.2Ontology language: OWL (Ontology Web Language)Ontology Parser: Jena 2.1Input source ontologies: Semantic Web Research Community (SWRC) ontology: 24 conceptsManually created a similar concept as SWRC ontology: 20 conceptsRequest instance: 6 8 conceptsResult</p></li><li><p>ConclusionNew challengesOnline mappingEfficiencySpace limitationKnowledge propagationPartial ontology mappingFuture workExperimentsContextResource instances selection</p></li><li><p>Q &amp; A</p></li></ul>

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