# Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation

Source: [Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation](<https://www.sota2.com/research/paper/68fa694d1e209131cea215a1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:57:51.173Z

Authors: Abhishek V. Potnis, Rajat C. Shinde, Surya S. Durbha

Published: 2021-01-23T06:12:20.000Z

## Abstract

With an increase in Geospatial Linked Open Data being adopted and published over the web, there is a need to develop intuitive interfaces and systems for seamless and efficient exploratory analysis of such rich heterogeneous multi-modal datasets. This work is geared towards improving the exploration process of Earth Observation (EO) Linked Data by developing a natural language interface to facilitate querying. Questions asked over Earth Observation Linked Data have an inherent spatio-temporal dimension and can be represented using GeoSPARQL. This paper seeks to study and analyze the use of RNN-based neural machine translation with attention for transforming natural language questions into GeoSPARQL queries. Specifically, it aims to assess the feasibility of a neural approach for identifying and mapping spatial predicates in natural language to GeoSPARQL's topology vocabulary extension including - Egenhofer and RCC8 relations. The queries can then be executed over a triple store to yield answers for the natural language questions. A dataset consisting of mappings from natural language questions to GeoSPARQL queries over the Corine Land Cover(CLC) Linked Data has been created to train and validate the deep neural network. From our experiments, it is evident that neural machine translation with attention is a promising approach for the task of translating spatial predicates in natural language questions to GeoSPARQL queries.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.09427>)

[PDF](<https://arxiv.org/pdf/2101.09427.pdf>)