Improving the Computational Performance of Ontology-Based Classification Using Graph Databases
Abstract
:1. Introduction
2. Related Work
2.1. Remote Sensing and Ontologies
2.2. Graph Databases and Applications
2.3. Advantages of Graph-Based Approaches for Classification Tasks
3. Data and Methods
3.1. Remote Sensing Data and the Associated Ontology
Sample Sizes | Residential Buildings | Industrial Buildings | Other Buildings |
---|---|---|---|
100 | 25 | 25 | 50 |
250 | 50 | 50 | 150 |
500 | 125 | 125 | 250 |
1000 | 250 | 250 | 500 |
2500 | 500 | 500 | 1500 |
5000 | 1250 | 1250 | 2500 |
10,000 | 2500 | 2500 | 5000 |
<EquivalentClasses> <Class IRI="#PitchedRoof"/> <DataSomeValuesFrom> <DataProperty IRI="#slope"/> <DatatypeRestriction> <Datatype abbreviatedIRI="xsd:double"/> <FacetRestriction facet="&xsd;minInclusive"> <Literal datatypeIRI="&xsd;double">25.0</Literal> </FacetRestriction> </DatatypeRestriction> </DataSomeValuesFrom> </EquivalentClasses>
3.2. Neo4j-Based Classification Workflow
MATCH (startTable { name:'#ResidentialArea'}),(endTable:Equivalent_Class_Data_Values), paths = (startTable)-[*..15]->(endTable) return filter(x IN nodes(paths) WHERE x:Equivalent_Class_Data_Values)
MATCH (startTable { name:'#ResidentialArea' }),(endTable:Individual), paths = (startTable)-[*..15]->(endTable) return DISTINCT filter(x IN nodes(paths) WHERE x:Individual)
Procedure CLASSIFICATION Input: Individuals, Restrictions begin PROPERTY_CHECK 1: while (Individuals) 2: while (Individual_Properties) 3: if (Individual_Property MATCHES Restriction) then SET Classified 4: else SET Not_Classified and break 5: end if 6: end while 7: end while end PROPERTY_CHECK Output: Classified Individuals
3.3. RDF/SPARQL-Based Approach
4. Benchmarking of Classification Performance
4.1. Classification Results for Protégé-Based Reasoners vs. the Neo4j-Based Approach
4.2. Classification Results for the Neo4j-Based Approach vs. the RDF/SPARQL-Based Approach
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Lampoltshammer, T.J.; Wiegand, S. Improving the Computational Performance of Ontology-Based Classification Using Graph Databases. Remote Sens. 2015, 7, 9473-9491. https://doi.org/10.3390/rs70709473
Lampoltshammer TJ, Wiegand S. Improving the Computational Performance of Ontology-Based Classification Using Graph Databases. Remote Sensing. 2015; 7(7):9473-9491. https://doi.org/10.3390/rs70709473
Chicago/Turabian StyleLampoltshammer, Thomas J., and Stefanie Wiegand. 2015. "Improving the Computational Performance of Ontology-Based Classification Using Graph Databases" Remote Sensing 7, no. 7: 9473-9491. https://doi.org/10.3390/rs70709473