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.