research activity including geographical ontology modules for efficient semantic web reuse
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Research Activity including Geographical Ontology Modules for Efficient Semantic Web Reuse. David George, University of Central Lancashire. Research Activities. Semantic Heterogeneity Structural and Semantic discrepancies in database conceptualisation and development - PowerPoint PPT PresentationTRANSCRIPT
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Research Activity
including
Geographical Ontology Modules forEfficient Semantic Web Reuse
David George, University of Central Lancashire
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Research Activities
• Semantic Heterogeneity
– Structural and Semantic discrepancies in database conceptualisation and development
• Data and Information Integration
– Federated Databases
– Mediators: Global-as-View, Local-as-View
– Information Brokering Systems and use of Ontology
• Semantic Web and Ontology
• Practical interaction with Semantic Web Technologies
– Protégé, FaCT++, SWOOP, and Jena API Toolkit
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Research Activities
• Development of Jena-based Java Browser Interface: inc
– Reading OWL and querying SPARQL
– RDF storage in MySQL
• Foundation Ontology: SUMO, DOLCE, CyC, BFO (Snap and Span)
• Design Best-Practice: Modularity in Ontology development (Rector, 2003)
• Experimentation with small-scale OWL ontologies
• Formal Concept Analysis - using Concept Explorer
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Structural & Semantic Heterogeneity
• Abstraction Level Conflicts– generalisation/specialisation/aggregation
• Schematic Discrepancies– Objects represented differently
– Data, attributes, entity
• Entity Definition Conflicts– naming conflicts (synonyms and homonyms)
– database identifier conflicts e.g. id# v. name
• Data Value Conflicts– temporal Inconsistency (last update)
– data representation (integer v. string/precision/scale)
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Data Integration
System
Knowledge
Data
Information
Federated DBS Federated IS (inc Mediators) Information Brokering
Global DomainAgreements
Global DomainAgreements
Local TaskSchemas
Local TaskSchemas
1985 1995
Digital mediaVisual/Spatial/Temporal Data[Kiosk/Geographic/Flights/Forecasting]
Structured,Semi-structuredText repositories
Structured DBs, Files
Virtual IntegrationSingle Ontologies
Multiple ontologies,Inter-ontological
Focus – SemanticsDomain-specific
Focus – Systems& Communications
Schema IntegrationCommon Data Models
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Jena Toolkit – OWL interface
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Ontology Specification: Best Practice
Ontology elements can be described as:
RailwayBridge ≡ Bridge ⊓ (hasForm ∃ Structure ⊓ hasRole ∃ RailTransportRole)
– Primitives: self-standing entities (objects/forms) e.g. Structure, Process, System, Organisation
– Relations: concept-linking properties e.g. X hasForm Y, hasRole …
– Roles: functions e.g. RailTransportRole
and– Definables: dependent concepts defined by combining Primitives,
Relations, and Roles:
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Formal Concept Analysis
• Using Concept Explorer
• Examined how Concept Analysis may be useful in identifying Classes and Instances in database tables
• Considered structural heterogeneity:
– Classes represented by single entity (table)
– Classes represented by table joins
– Classes as subset of table records
– Instances represented by entity, attribute, data (record)
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Formal Concept Analysis
Example:
Classes represented by table joins
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Creating Geographical Ontology Modules for
Efficient Semantic Web Reuse
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Ontology and Integration
• Ontology Reuse is a key Integration benefit (Noy and Hafner, 1997 ).
• Ontology development still at a stage where little interchange between organisations?
• Merger, Alignment and Mapping complexity issues with Integration.
• Developer reluctance – easier to re-invent own local ontology than reuse.
• Reuse of an external ontology will likely result in descriptive and structural irrelevances.
• Smaller component ontology modules –improvised as required – may encourage wider usage/take-up
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Ontology Integration
Possible Ontology [ On ] Objectives
1. Merger: OA + OB → OC
2. Alignment: OA ≡ OB ≡ OC
3. Mapping: a virtual integration where OA, OB and OC concepts are semantically related.
Methods– 1 and 2 are achieved by rewriting (reformulation).
– Original ontologies are subsumed or made consistent (respectively).
– 3 is achieved by mappings between concepts of imported ontologies. A, B and C endure autonomously.
– Ontology Reuse, in this presentation, refers to 3: Mapping.
(Pinto et al., 1999, Noy and Musen, 1999, de Bruijn et al., 2004, Visser and Tamma, 1999, Kalfoglou and Schorlemmer, 2003, Ding et al., 2002)
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1 - “Informal” specific Class Reuse
• Using namespace declaration to explicitly specify a single external concept, e.g.
<rdf:RDF xmlns="http://www.livewiredg.myby.co.uk/rdf/geo-layers/rail.owl#" xmlns:cyc="http://www.cyc.com/2003/04/01/cyc#" > <owl:Class rdf:about="&cyc;TransportationCompany"/> <owl:Class rdf:ID="RailOperator"> <rdfs:subClassOf rdf:resource="#RailwayComponent"/> <rdfs:subClassOf rdf:resource="&cyc;TransportationCompany"/> </owl:Class> ……..
<rdf:RDF xmlns="http://www.livewiredg.myby.co.uk/rdf/geo-layers/rail.owl#" xmlns:cyc="http://www.cyc.com/2003/04/01/cyc#" > <owl:Class rdf:about="&cyc;TransportationCompany"/> <owl:Class rdf:ID="RailOperator"> <rdfs:subClassOf rdf:resource="#RailwayComponent"/> <rdfs:subClassOf rdf:resource="&cyc;TransportationCompany"/> </owl:Class> ……..
• How would an agent understand the Cyc context of the superclass of “cyc:TransportationCompany”
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2 - “Formalised” specific Class Reuse
E-Connections
• Representation and reasoning with foreign ontologies (Grau et al, 2006)
• Allows specific concept linking. Few tools available e.g. SWOOP (OWL Ontology Editor)
<rdf:RDF xmlns:global="http://www.livewiredg.myby.co.uk/rdf/geo-layers/global.owl#" xmlns=http://www.owl-ontologies.com/flight.owl# ……..>
<owl:Class rdf:about=“&global;Artifact"/> <owl:Class rdf:ID="Helicopter"> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty> <owl:LinkProperty rdf:about="#hasForm"/> </owl:onProperty> <owl:someValuesFrom rdf:resource="&global;Artifact"/> </owl:Restriction> </rdfs:subClassOf> </owl:Class>
<owl:LinkProperty rdf:ID="hasForm"> <owl:foreignOntology rdf:resource="&global;"/> <rdfs:domain rdf:resource="#Helicopter"/> <rdfs:range> <owl:foreignClass rdf:about="&global;Artifact"> <owl:foreignOntology rdf:resource="&global; "/> </owl:foreignClass> </rdfs:range> </owl:LinkProperty>
<rdf:RDF xmlns:global="http://www.livewiredg.myby.co.uk/rdf/geo-layers/global.owl#" xmlns=http://www.owl-ontologies.com/flight.owl# ……..>
<owl:Class rdf:about=“&global;Artifact"/> <owl:Class rdf:ID="Helicopter"> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty> <owl:LinkProperty rdf:about="#hasForm"/> </owl:onProperty> <owl:someValuesFrom rdf:resource="&global;Artifact"/> </owl:Restriction> </rdfs:subClassOf> </owl:Class>
<owl:LinkProperty rdf:ID="hasForm"> <owl:foreignOntology rdf:resource="&global;"/> <rdfs:domain rdf:resource="#Helicopter"/> <rdfs:range> <owl:foreignClass rdf:about="&global;Artifact"> <owl:foreignOntology rdf:resource="&global; "/> </owl:foreignClass> </rdfs:range> </owl:LinkProperty>
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3 - “Modularity” by sub-domain separation
• SWOOP permits ontology partitioning (module extraction)
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4 - Class reuse by Ontology Import
Objective:
Map Rail Ontology class “RailOperator” to Cyc Ontology class “TransportationCompany”
Action:
Import Opencyc into Rail > 6.8MB
Effect:
Adds:2843 classes1256 propertiesload time 1.5 to 7.5 minsProtégé “out of memory”
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Alternative Reuse approach?
• Consider the way Ontologies conceptualised and developed?
• Break down domain ontologies into sub-domains (modules)
• Try to achieve disjoint structures – minimise redundancy
• Can be demonstrated using Geographical context
• Geographical concepts interface with virtually every aspect of daily life and feature prominently in information management systems.
• Geographical ontologies offer a logical vehicle, to examine how modules can be specified efficiently and effectively.
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Ontological Inefficiency
• Ontology Reuse - Imports– E.g. if OTN 1 is imported: what do we
see?– Ontology much smaller than Cyc, but still
multiple sub-domains
• Potential redundancy• Vulnerability to change• How relevant are they?
• Only for an application that uses ALL concepts
• Only for an application that uses ALL concepts
1 OTN - Ontology of Transportation Networks (Lorenz et al, 2005)
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Ontology Permanence
Fixed Classes
Variable Classes
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Transportation Tourism
Ontology “Geo-Modules”
Geo-Modules
Multi-modal
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Land Transport
multimodal
single-mode ?
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Transport Interchange
• multimodal: road-rail • within a town, service facility
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Visualising Our Transportation Domain
M67M6
A6
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Rail Transport Ontology
City
endsAt*
startsFrom*
Road domain
PopulationGroupConcept
City
endsAt*
startsFrom*
Q: rename LevelCrossing → RoadCrossing? But we don’t do Roads in Rail!
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Road Transport Ontology
Rail domain
Q: reclassify ChannelTunnelTerminal → Road Concept? But we don’t do Rail in Roads!
PopulationGroupConcept
City
endsAt*
startsFrom*
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LandTransport Ontology
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LandTransport: Import Consequences
• We would need to import: Road, Rail, PopGroups into LandTransport
• For just Road and Rail it results in duplications and redundancy
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Revisualisation: Transportation Layers
M67M6
A6
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How do we develop “Geo-Modules”
• Need to “de-integrate” to allow low-cost integration
• Aim towards “effectively” disjoint domains
• Deliver by removing concept duplication between modules – redundancy
• Need to promote/relegate multi or single-context concepts and relations
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Transportation Domain Layers
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Modular Ontology: +ve/-ve
• Advantages– Small is manageable– Select only required building block modules– Independent therefore less vulnerable to change– Change is isolated to the module and subsuming
domain?
• Disadvantages– Increased mappings?– Needs to be examined
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References
DE BRUIJN, J., DING, Y., ARROYO, S. & FENSEL, D. (2004) Semantic Information Integration in the COG project [online]. Digital Enterprise Research Institute (DERI), University of Innsbruck. Available from: http://www.cogproject.org/publications/sii-wp.pdf. [Accessed 19 December 2004].
DING, Y., FENSEL, D., KLEIN, M. & OMELAYENKO, B. (2002) The semantic web: yet another hip? Data & Knowledge Engineering, 41(2), pp. 205-227.
DING, Y. & FOO, S. (2002) Ontology Research and Development: Part 2 - A Review of Ontology mapping and evolving. Journal of Information Science, 28(5), pp. 383-396.
GRAU, B. C., PARSIA, B. & SIRIN, E. (2006) Combining OWL ontologies using E-Connections. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 4(1), pp. 40-59.
KALFOGLOU, Y. & SCHORLEMMER, M. (2003) Ontology mapping: the state of the art. The Knowledge Engineering Review, 18(1), pp. 1-31.
NOY, N. F. & HAFNER, C. D. (1997) The State of the Art in Ontology Design - A Survey and Comparative Review. AI Magazine, 18(3), pp. 53-74.
NOY, N. F. & MUSEN, M. A. (1999) SMART: Automated Support for Ontology Merging and Alignment Stanford, MA, Stanford Medical Informatics. Available from: http://www-smi.stanford.edu/pubs/SMI_Reports/SMI-1999-0813.pdf. [Accessed 22 December 2004].
PINTO, H. S., GÓMEZ-PÉREZ, A. & MARTINS, J. P. (1999) Some Issues on Ontology Integration. In: Proceedings of IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden, August 2 1999. CEUR-WS, pp. 7.1-7.12.
RECTOR, A. L. (2003) Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. In: Proceedings of 2nd International Conference On Knowledge Capture. Sanibel Island, FL, USA, 2003. ACM Press, New York, NY, USA, pp. 121-128.
VISSER, P. R. S. & TAMMA, V. A. M. (1999) An Experience with Ontology-based Agent Clustering. In: Proceedings of IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden, 2 August 1999. CEUR-WS, pp. 12.1-12.13.