A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends
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Intelligent Interaction Research Group, Cultural Technology Department, University of the Aegean, 81100 Lesbos, Greece
2
Humanistic and Social Informatics Lab, Department of Informatics, Ionian University, 49100 Kerkira, Greece
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(3), 59; https://doi.org/10.3390/a13030059
Received: 14 January 2020
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Revised: 23 February 2020
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Accepted: 27 February 2020
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Published: 4 March 2020
(This article belongs to the Special Issue Mining Humanistic Data 2019)
Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.
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Keywords:
sharing economy; short-term rentals; Airbnb; Athens; Greece; geolocation ontology; ghost hotel discovery; rapid automatic keyword extraction
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MDPI and ACS Style
Alexandridis, G.; Voutos, Y.; Mylonas, P.; Caridakis, G. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms 2020, 13, 59. https://doi.org/10.3390/a13030059
AMA Style
Alexandridis G, Voutos Y, Mylonas P, Caridakis G. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms. 2020; 13(3):59. https://doi.org/10.3390/a13030059
Chicago/Turabian StyleAlexandridis, Georgios, Yorghos Voutos, Phivos Mylonas, and George Caridakis. 2020. "A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends" Algorithms 13, no. 3: 59. https://doi.org/10.3390/a13030059
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