{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:31:15Z","timestamp":1772771475463,"version":"3.50.1"},"reference-count":0,"publisher":"Copernicus GmbH","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AGILE GIScience Ser."],"abstract":"<jats:p>Abstract. There is an increasing trend of applying AIbased automated methods to geoscience problems. An important example is a geographic question answering (geoQA) focused on answer generation via GIS workflows rather than retrieval of a factual answer. However, a representative question corpus is necessary for developing, testing, and validating such generative geoQA systems. We compare five manually constructed geographical question corpora, GeoAnQu, Giki, GeoCLEF, GeoQuestions201, and Geoquery, by applying a conceptual transformation parser. The parser infers geo-analytical concepts and their transformations from a geographical question, akin to an abstract GIS workflow. Transformations thus represent the complexity of geo-analytical operations necessary to answer a question. By estimating the variety of concepts and the number of transformations for each corpus, the five corpora can be compared on the level of geo-analytical complexity, which cannot be done with purely NLP-based methods. Results indicate that the questions in GeoAnQu, which were compiled from GIS literature, require a higher number as well as more diverse geo-analytical operations than questions from the four other corpora. Furthermore, constructing a corpus with a sufficient representation (including GIS) may require an approach targeting a uniquely qualified group of users as a source. In contrast, sampling questions from large-scale online repositories like Google, Microsoft, and Yahoo may not provide the quality necessary for testing generative geoQA systems. \n                    <\/jats:p>","DOI":"10.5194\/agile-giss-4-10-2023","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T10:41:30Z","timestamp":1686134490000},"page":"1-10","source":"Crossref","is-referenced-by-count":1,"title":["Semantic complexity of geographic questions - A comparison in terms of conceptual transformations of answers"],"prefix":"10.5194","volume":"4","author":[{"given":"Enkhbold","family":"Nyamsuren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric J.","family":"Top","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Scheider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niels","family":"Steenbergen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3145","published-online":{"date-parts":[[2023,6,6]]},"container-title":["AGILE: GIScience Series"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/4\/10\/2023\/agile-giss-4-10-2023.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T13:36:40Z","timestamp":1686749800000},"score":1,"resource":{"primary":{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/4\/10\/2023\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/agile-giss-4-10-2023","relation":{},"ISSN":["2700-8150"],"issn-type":[{"value":"2700-8150","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,6]]}}}