Journal cover Journal topic
AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
Journal topic
Volume 1
AGILE GIScience Ser., 1, 13, 2020
https://doi.org/10.5194/agile-giss-1-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
AGILE GIScience Ser., 1, 13, 2020
https://doi.org/10.5194/agile-giss-1-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2020

15 Jul 2020

Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

Gengchen Mai1, Krzysztof Janowicz1, Sathya Prasad2, Meilin Shi1, Ling Cai1, Rui Zhu1, Blake Regalia1, and Ni Lao3 Gengchen Mai et al.
  • 1STKO Lab, UC Santa Barbara, Santa Barbara, CA, USA
  • 2ESRI Inc, Redlands, CA, USA
  • 3SayMosaic Inc, Palo Alto, CA, USA

Keywords: Query Expansion, ArcGIS Online, Semantically Enriched Search Engine, Geoportal, Geographic Information Retrieval

Abstract. Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user’s search intentions. To better understand a user’s search intention, query expansion can be used to enrich the user’s query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user’s query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user’s query. This semantic query expansion framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user’s search intention and significantly outperforms a well-established baseline – Lucene’s practical scoring function – with more than 3.0 increments in DCG@K (K=3,5,10).

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