README.md

---
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---

<div align="center">
  <br>
  <img align="center" src="https://cdn-uploads.huggingface.co/production/uploads/65aa8369f8111f40c03e3c61/0ZohMluOG70HW1irfGCAb.png" alt="GeoQuestions1089" width="400"/>
  </div>
<br>
<br>
<div align="center">
A crowdsourced geospatial question-answering dataset that contains 1089 triples of natural language questions, SPARQL/GeoSPARQL queries, and their answers over YAGO2geo.
</div> 

## Overview

**GeoQuestions1089** is a crowdsourced geospatial question-answering dataset that targets the Knowledge Graph [YAGO2geo](https://yago2geo.di.uoa.gr/). It contains 1089 triples of geospatial questions, their answers, and the respective SPARQL/GeoSPARQL queries. 

It has been used to benchmark two state of the art Question Answering engines, [GeoQA2](https://github.com/AI-team-UoA/GeoQA2) and [the engine of Hamzei et al](https://github.com/hamzeiehsan/Questions-To-GeoSPARQL).

## Repository information

The official code repository for this dataset is located on [GitHub](https://github.com/AI-team-UoA/GeoQuestions1089/).

## Dataset

The dataset is described in the following [paper](http://cgi.di.uoa.gr/~koubarak/publications/2023/ISWC_2023_GeoQuestions_paper-3.pdf) (also used to cite the dataset):

```
@inproceedings{10.1007/978-3-031-47243-5_15,
  title = {Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089},
  author = {Sergios-Anestis Kefalidis, Dharmen Punjani, Eleni Tsalapati, 
         Konstantinos Plas, Mariangela Pollali, Michail Mitsios, 
         Myrto Tsokanaridou, Manolis Koubarakis and Pierre Maret},
  booktitle = {The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference,
            Athens, Greece, November 6-10, 2023, Proceedings, Part {II}},
  year = {2023}
}
```

Shortly, the *GeoQuestions1089* dataset consists of two parts, which we will refer to as *GeoQuestions_c* and *GeoQuestions_w* both of which target the union of YAGO2 and YAGO2geo. 

*GeoQuestions_c* consits of 1017 entries and *GeoQuestions_w* of 72 entries. The difference between the two is that the natural language questions of *GeoQuestions_w* contain grammatical, syntactical and spelling mistakes.

<!-- <center> -->

| Description | Range |
| --- | --- |
|Triples targeting YAGO2geo (*GeoQuestions_c*) | 1-895 |
|Triples targeting YAGO2 + YAGO2geo (*GeoQuestions_c*) | 896-1017 |
|Triples with questions that contain mistakes (*GeoQuestions_w*) | 1018-1089 |

<!-- </center> -->

### Current version of the dataset
The aforementioned paper describes version 1.0. The latest available version is 1.1.

Version 1.1 includes several enhancements:
- Uniform query format and variable naming
- Fixes in natural language capitalization
- Corrections in query categorization
- Replacement of stSPARQL functions with GeoSPARQL functions where applicable
- Minor improvements in query correctness of existing queries
- A few triples that were erroneous (resulting from incorrect file modifications and text editing) have been replaced by correct ones.

These updates ensure greater consistency and accuracy in the dataset, making it a more reliable resource for geospatial QA research.

## Categories

The questions of the dataset are split into 9 categories:

<ol type="A">
  <li>Asking for a thematic or a spatial attribute of a feature, e.g., 
    <i>Where is Loch Goil located?</i></li>
  <li>Asking whether a feature is in a geospatial relation with another feature or features, e.g., 
    <i>Is Liverpool east of Ireland?</i></li>
  <li>Asking for features of a given class that are in a geospatial relation with another feature, e.g., 
    <i>Which counties border county Lincolnshire?</i></li>
  <li>Asking for features of a given class that are in a geospatial relation with any features of another class, e.g., 
    <i>Which churches are near castles?</i></li>
  <li>Asking for features of a given class that are in a geospatial relation with an unspecified feature of another class, and either one or both, is/are in another geospatial relation with a feature specified explicitly, e.g., 
    <i>Which churches are near a castle in Scotland?</i></li>
  <li>As in categories C, D and E above, plus more thematic and/or geospatial characteristics of the features expected as answers, e.g.,
    <i>Which mountains in Scotland have height more than 1000 meters?</i></li>
  <li>Questions with quantities and aggregates, e.g.,
    <i>What is the total area of lakes in Monaghan?</i> or <i>How many lakes are there in Monaghan?</i></li>
  <li>Questions with superlatives or comparatives, e.g.,
    <i>Which is the largest island in Greece?</i></li>
  <li>Questions with quantities, aggregates, and superlatives/comparatives, e.g.,
    <i>Which city in the UK has the most hospitals?</i></li> 
</ol>

<!-- <center> -->

| Category | GeoQuestions1089_c | GeoQuestions1089_w |
|----------|--------------------|--------------------|
| A        | 173                | 16                 |
| B        | 139                | 11                 |
| C        | 176                | 14                 |
| D        | 22                 | 1                  |
| E        | 138                | 6                  |
| F        | 24                 | 2                  |
| G        | 174                | 11                 |
| H        | 145                | 9                  |
| I        | 26                 | 2                  |

<!-- </center> -->

You can read more about these categories in the [paper](http://cgi.di.uoa.gr/~koubarak/publications/2023/ISWC_2023_GeoQuestions_paper-3.pdf).

## Benchmark (Version 1.1)

We have used the dataset to evaluate the engines [GeoQA2](https://github.com/AI-team-UoA/GeoQA/tree/geoqa2) and the engine of [Hamzei et al.](https://github.com/hamzeiehsan/Questions-To-GeoSPARQL#translating-place-related-questions-to-geosparql-queries-thewebconf-2022). We present the results of the evaluation:

### GeoQA2

#### Combined Table: Evaluation of GeoQA2 over GeoQuestions_C and GeoQuestions_W

|           | Category | Executable Queries (C) | Correct Answers (C) | Correct Answers*(1) (C) | Executable Queries (W) | Correct Answers (W) | Correct Answers*(1) (W) |
|-----------|----------|------------------------|---------------------|----------------------|------------------------|---------------------|----------------------|
|           | A        | 83.81%                 | 50.86%              | 60.68%               | 75.00%                 | 50.00%              | 66.67%               |
|           | B        | 74.82%                 | 60.43%              | 80.76%               | 81.81%                 | 45.45%              | 55.56%               |
|           | C        | 81.25%                 | 45.45%              | 55.94%               | 85.71%                 | 50.00%              | 58.34%               |
|           | D        | 54.54%                 | 9.09%               | 16.67%               | 100.00%                | 0.00%               | 0.00%                |
|           | E        | 76.08%                 | 24.63%              | 32.38%               | 50.00%                 | 33.33%              | 66.67%               |
|           | F        | 58.33%                 | 25.00%              | 42.85%               | 50.00%                 | 0.00%               | 0.00%                |
|           | G        | 73.56%                 | 33.33%              | 45.31%               | 36.36%                 | 0.00%               | 0.00%                |
|           | H        | 66.89%                 | 18.62%              | 27.83%               | 66.67%                 | 0.00%               | 0.00%                |
|           | I        | 80.76%                 | 19.23%              | 23.80%               | 50.00%                 | 0.00%               | 0.00%                |
||   **Total**         | 75.61%                 | 37.75%              | 49.93%               | 68.05%                 | 30.55%              | 44.89%               |

##### (1) Corrent Answers* is the percentage of correct answers calculated over the number of Executable Queries generated by the engines.

### System of Hamzei et al.

#### Combined Table: Evaluation of the system of Hamzei et al. over GeoQuestions_C and GeoQuestions_W


|           | Category | Executable Queries (C) | Correct Answers (C) | Correct Answers* (C) | Executable Queries (W) | Correct Answers (W) | Correct Answers* (W) |
|-----------|----------|------------------------|---------------------|----------------------|------------------------|---------------------|----------------------|
|           | A        | 82.08%                 | 23.12%              | 28.16%               | 93.75%                 | 6.25%               | 6.67%                |
|           | B        | 94.96%                 | 53.23%              | 56.06%               | 100.00%                | 54.54%              | 54.54%               |
|           | C        | 81.81%                 | 26.13%              | 31.94%               | 100.00%                | 14.28%              | 14.28%               |
|           | D        | 81.81%                 | 4.54%               | 5.55%                | 100.00%                | 0.00%               | 0.00%                |
|           | E        | 92.75%                 | 6.52%               | 7.03%                | 83.34%                 | 0.00%               | 0.00%                |
|           | F        | 62.50%                 | 12.50%              | 20.00%               | 90.90%                 | 0.00%               | 0.00%                |
|           | G        | 80.45%                 | 10.34%              | 12.85%               | 100.00%                | 0.00%               | 0.00%                |
|           | H        | 77.93%                 | 26.89%              | 34.51%               | 77.78%                 | 0.00%               | 0.00%                |
|           | I        | 84.61%                 | 7.96%               | 9.09%                | 50.00%                 | 0.00%               | 0.00%                |
|  |  **Total**        | 83.97%                 | 22.81%              | 27.28%               | 93.05%                 | 12.50%              | 13.43%               |


##### Additional benchmark results exist and we are working on publishing them. Until then, if you want to see more please send a message at: 
`s[dot]kefalidis[at]di[dot]uoa[dot]gr`


## Materialization and Transpiler

To improve the time performance of query execution, we pre-computed and materialized certain relations between entities in the YAGO2geo KG. 

The geospatial relations *within*, *crosses*, *intersects* and *borders* (and their extensions, e.g., *overlaps* and *covers*) are the most expensive ones to be computed. While *north*, *south*, *east* and *west* are easily computed. Hence, we materialized these relations.

To easily utilize these materialized relations, please see the [GitHub repository](https://github.com/AI-team-UoA/GeoQuestions1089/).

## RDF Store

To run the experiments and generate the answers for the gold and generated queries we used GraphDB. Because GraphDB does not support stSPARQL functions, we have [extended the GeoSPARQL plugin of GraphDB](https://github.com/SKefalidis/graphdb-geosparql-plugin).

## Notes

### About the definition of near for distance calculations
We decided to define *near* based on the concept used. This is consistent with the definition of near in [GeoQuestions201](https://geoqa.di.uoa.gr/geospatial_gold_standard.html).

| Near to | Distance |
| --- | --- |
|Near to a City: | 5km |
|Near to a Town: | 5km |
|Near to a Bay: | 1km |
|Near to a Beach: | 1km |
|Near to a Forest: | 1km |
|Near to a Hotel: | 1km |
|Near to a Lake: | 1km |
|Near to a Landmark: | 1km |
|Near to a Village: | 1km |
|Near to a Restaurant: | 500 meters |
|Near to a Park: | 500 meters|

### Prefixes used in GeoQuestions1089:

```
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX yago: <http://yago-knowledge.org/resource/>
PREFIX y2geor: <http://kr.di.uoa.gr/yago2geo/resource/>
PREFIX y2geoo: <http://kr.di.uoa.gr/yago2geo/ontology/>
PREFIX strdf: <http://strdf.di.uoa.gr/ontology#>
PREFIX uom: <http://www.opengis.net/def/uom/OGC/1.0/>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
```

## License

Released under the CC0 Attribution 4.0 International license.

Copyright © 2024 AI-Team, University of Athens