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

TL;DR

This paper presents a semantically-enriched search engine for geoportals, enhancing user query understanding by integrating geospatial and thematic semantic expansions, leading to significantly improved search relevance in ArcGIS Online.

Contribution

It introduces a novel semantic query expansion framework combining geospatial and thematic insights, specifically tailored for geographic information retrieval in geoportals.

Findings

Semantic expansion improves search relevance by over 3 DCG@K points.

The framework effectively captures user search intentions.

Significant performance gains over baseline Lucene search.

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 1 2 G. Mai et al. 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).