Bruno Martins
ABSTRACT
This vision paper argues that a geospatial knowledge base combining textual descriptions for concepts such as places, together with place types, semantic relations between concepts and, most importantly, polygonal geometries associated to the geospatial concepts, constitutes a valuable resource for researchers working on the computational modeling of spatial language. We describe a simple procedure for producing one such resource from existing open datasets, and discuss possible ways for moving beyond the current state-of-the-art within the general area of geospatial text mining, through studies supported by one such knowledge base.
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Digital Library
- A. Khan, M. Vasardani, and S. Winter. Extracting spatial information from place descriptions. In ACM SIGSPATIAL Workshop on Computational Models of Place, 2013. Google ScholarDigital Library
- J. Nothman, N. Ringland, W. Radford, T. Murphy, and J. R. Curran. Learning multilingual named entity recognition from Wikipedia. Artif. Intell., 194, 2013. Google ScholarDigital Library
- J. Pustejovsky, P. Kordjamshidi, M.-F. Moens, A. Levine, S. Dworman, and Z. Yocum. SemEval-2015 Task 8: SpaceEval. In International Workshop on Semantic Evaluation, 2015.Google ScholarCross Ref
- S. Roller, M. Speriosu, S. Rallapalli, B. Wing, and J. Baldridge. Supervised text-based geolocation using language models on an adaptive grid. In Conference on Empirical Methods in Natural Language Processing, 2012. Google ScholarDigital Library
- M. Speriosu and J. Baldridge. Text-driven toponym resolution using indirect supervision. In Annual Meeting of the Association for Computational Linguistics, 2013.Google Scholar
- J. O. Wallgrün, A. Klippel, and T. Baldwin. Building a corpus of spatial relational expressions extracted from Web documents. In ACM SIGSPATIAL Workshop on Geographic Information Retrieval, 2014. Google ScholarDigital Library
Index Terms
- Expanding the utility of geospatial knowledge bases by linking concepts to WikiText and to polygonal boundaries
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