Abstract
Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. In previous work we proposed a system-oriented logic-based formalization of place that goes beyond place names by introducing composition patterns of place which enable function-based search of space. In this study, we introduce flexibility into these patterns in terms of what is included when describing the spatial composition of a place using two distinct approaches, based on modal and probabilistic logic. Additionally, we propose a novel automated process of extracting these patterns relying on both theoretical and empirical knowledge, using statistical and spatial analysis and statistical relational learning. The proposed methodology is exemplified through the use case of locating all areas within London that support shopping-related functionality. Results show that the newly introduced patterns can identify more relevant areas, additionally offering a more fine-grained representation of the level of support of the required functionality.
Highlights
People live in and act on space but deal and interact with place; Curry [1] argues that place is a human invention to describe space
We evaluate the potential benefits of these contributions to place search by investigating how each of the three different patterns can enable a place search system to locate all places in London, UK that support functionality similar to a shopping mall
Empirical patterns provide the capability to express that a composition rule is necessary or possible, while probabilistic patterns attach numerical weights to each composition rule
Summary
People live in and act on space but deal and interact with place; Curry [1] argues that place is a human invention to describe space. Within the domain of Geographical Information Science ( GIScience), place is the result of combining space, as defined in mathematics and physics, with human experience [2]. Two of the most fundamental queries that GIScience is tasked to address regarding spatial information are the localization and identification or categorization of places (e.g., “where is that” and “what is there”). A prominent approach is to use gazetteers [3], which treat place as typed place names associated spatial footprints, sometimes augmented with further semantics using ontologies. Gazetteers predominantly focus on thematic and spatial information and are unable to capture how people interact with places. Other approaches rely on narratives to extract place-related information [4] about place localization in the form of qualitative spatial relations associated with known locations; as with gazetteers, their focus is solely on locating places of interest. Current place search approaches are not well-equipped to answer queries accurately and convincingly such as “locate shopping areas, even if they are not explicitly denoted as shopping malls”
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