{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:25:31Z","timestamp":1760243131720,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,7,22]],"date-time":"2015-07-22T00:00:00Z","timestamp":1437523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Austrian Science Fund (FWF)","award":["DK W 1237N23"],"award-info":[{"award-number":["DK W 1237N23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing availability of very high-resolution remote sensing imagery  (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities\u2014so-called individuals\u2014is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Prot\u00e9g\u00e9 environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed\u2014up to 80,000 times faster than the Prot\u00e9g\u00e9-based approach.<\/jats:p>","DOI":"10.3390\/rs70709473","type":"journal-article","created":{"date-parts":[[2015,7,22]],"date-time":"2015-07-22T08:45:40Z","timestamp":1437554740000},"page":"9473-9491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Improving the Computational Performance of Ontology-Based Classification Using Graph Databases"],"prefix":"10.3390","volume":"7","author":[{"given":"Thomas","family":"Lampoltshammer","sequence":"first","affiliation":[{"name":"School of Information Technology and Systems Management, Salzburg University of  Applied Sciences, Urstein S\u00fcd 1, Puch, Salzburg 5412, Austria"},{"name":"Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstrasse 30,  Salzburg 5020, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanie","family":"Wiegand","sequence":"additional","affiliation":[{"name":"IT Innovation Centre, University of Southampton, Gamma House, Enterprise Road,  Southampton SO16 7NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,22]]},"reference":[{"key":"ref_1","unstructured":"Baader, F., Horrocks, I., and Sattler, U. 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