Infrastructure Engineering - Research Publications

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    Neural factoid geospatial question answering
    Li, H ; Hamzei, E ; Majic, I ; Hua, H ; Renz, J ; Tomko, M ; Vasardani, M ; Winter, S ; Baldwin, T (UNIV MAINE, 2021)
    Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.
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    Spatial Concepts in the Conversation With a Computer
    Winter, S ; Baldwin, T ; Tomko, M ; Renz, J ; Kuhn, W ; Vasardani, M (ASSOC COMPUTING MACHINERY, 2021-07)
    Conversing about places with a computer poses a range of challenges to current AI.
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    Place questions and human-generated answers: A data analysis approach
    Hamzei, E ; Li, H ; Vasardani, M ; Baldwin, T ; Winter, S ; Tomko, M ; Kyriakidis, P ; Hadjimitsis, D ; Skarlatos, D ; Mansourian, A (Springer, Cham, 2020-01-01)
    This paper investigates place-related questions submitted to search systems and their human-generated answers. Place-based search is motivated by the need to identify places matching some criteria, to identify them in space or relative to other places, or to characterize the qualities of such places. Human place-related questions have thus far been insufficiently studied and differ strongly from typical keyword queries. They thus challenge today’s search engines providing only rudimentary geographic information retrieval support. We undertake an analysis of the patterns in place-based questions using a large-scale dataset of questions/answers, MS MARCO V2.1. The results of this study reveal patterns that can inform the design of conversational search systems and in-situ assistance systems, such as autonomous vehicles.
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    Starting to talk about place
    STIRLING, LESLEY ; CAVEDON, LAWRENCE ; RICHTER, DANIELA ; Winter, Stephen ; KEALY, ALLISON ; DUCKHAM, MATT ; RAJABIFARD, ABBAS ; RICHTER, KAI-FLORIAN ; Baldwin, Tim ( 2011)
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    Location-based mobile games for spatial knowledge acquisition
    Winter, S ; Richter, KF ; Baldwin, T ; Cavedon, L ; Stirling, L ; Duckham, M ; Kealy, A ; Rajabifard, A (CEMob2011, 2011-01-01)