A reasoning model for geo-referencing named and unnamed spatial entities in natural language place descriptions

Yousaf, Madiha; Wolter, Diedrich

doi:10.1080/13875868.2021.2002872

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…This chapter is based on content from the previously published paper by Yousaf and Wolter [407] in an interdisciplinary journal-Spatial cognition and computation in 2021, which includes a reasoning method to geo-reference both named and unnamed entities. The contributions of the corresponding first author includes conceptualization of dealing with unnamed spatial entities and developing a reasoning pipeline to interpret the spatial information through discussion with my supervisor, related work, paper writing and evaluation of the put-forth approach.…”

Section: G E O -R E F E R E N C I N G U S I N G S Pat I O -O N T O L ...mentioning

confidence: 99%

“…Experiments from the paper [407] have been re-organized and will be presented in detail in Chapter 7, respectively.…”

Section: G E O -R E F E R E N C I N G U S I N G S Pat I O -O N T O L ...mentioning

confidence: 99%

“…The experiments presented in this chapter are based on the content from the previously published paper by Yousaf and Wolter [407] in an interdisciplinary journal-Spatial cognition and computation in 2021. Furthermore, the experiments from a publication by Yousaf and Wolter in 2018 [405] are also reorganized and presented here.…”

Section: E X P E R I M E N T S a N D E Va L U At I O Nmentioning

confidence: 99%

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Representing and Reasoning with Context-Sensitive Vague Place Descriptions

Yousaf¹

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This doctoral thesis aims to design a geographic information system based on locations to make the information about the human location interpretable for computers. This research employs natural language place descriptions as its primary research input and focuses on five research components as main goals towards understanding place descriptions: incorporating context-sensitive information, spatial information extraction, and modeling, reasoning, place geo-referencing, and querying.

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Section: G E O -R E F E R E N C I N G U S I N G S Pat I O -O N T O L ...mentioning

confidence: 99%

“…Experiments from the paper [407] have been re-organized and will be presented in detail in Chapter 7, respectively.…”

Section: G E O -R E F E R E N C I N G U S I N G S Pat I O -O N T O L ...mentioning

confidence: 99%

Section: E X P E R I M E N T S a N D E Va L U At I O Nmentioning

confidence: 99%

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Representing and Reasoning with Context-Sensitive Vague Place Descriptions

Yousaf¹

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Automated Interpretation of Place Descriptions: Determining Entity Types for Querying OSM

2023

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This paper is concerned with interpretation of natural language place descriptions, as they are a rich source of geographic information. A place description is interpreted by matching geographic entities occurring in the text against the OpenStreetMap (OSM) database. This paper is mainly concerned with interpretation of paraphrased places, i.e., entities for which no name is given and which may only by described. Our objective is to determine suitable entity types that allow querying the OpenStreetMap database for the respective place. For example, if we wish to identify a place to eat, we have to check for entities of an a-priori unknown type (cafe, restaurant, etc.). Challenges arise from the open-endedness of language, its ambiguity, and context-sensitivity as well as from mismatches between human conceptualization of place and database ontologies. The contributions of this paper are, first, to present a hard problem that is key to geo-information retrieval beyond named entities. Second, we propose context-sensitive methods for identifying place types based on semantic word similarity. We evaluate the methods on text extracted from Wikipedia and travel blogs, revealing their contribution to advancing automated interpretation of place descriptions to paraphrased places.

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New Era for Geo-Parsing to Obtain Actual Locations: A Novel Toponym Correction Method Based on Remote Sensing Images

Wang

Yan²,

Zhu³

et al. 2022

Remote Sensing

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Geo-parsing, one of the key components of geographical information retrieval, is a process to recognize and geo-locate toponyms mentioned in texts. Such a process can obtain locations contained in toponyms successfully with consistent updating of neural network models and multiple contextual features. The significant offset distance between the geo-parsed locations and the actual occurrence locations still remains. This is because the geo-parsed locations sourced from toponyms in texts always point to the centers of cities, counties, or towns, and cannot directly represent the actual occurrence locations such as factories, farms, and activity areas. Consequently, The significant offset distances between the geo-parsed locations and the actual occurrence locations limit text mining applications in micro-scale geographic discoveries. This research aims at decreasing offset distances of geo-parsed locations by proposing a novel Toponym Correction Method based on satellite Remote Sensing Images (TC-RSI). The TC-RSI method uses satellite remote sensing images to provide extra detailed spatial information that can be associated with the sentence toponym by corresponding attributes. The TC-RSI method was validated in a case study of the forest ecological pattern dataset of An’hui province from visual, statistical, and robustness assessments. The correction results show that the TC-RSI method dramatically decreases the offset distances from about 50 km to about 1 km and promotes geographical discoveries on smaller scales. A series of analyses indicated that the TC-RSI is a valid, effective, and promising method to improve the accuracy of geo-parsed locations, which allows text mining to find more accurate geographical discoveries with lower offset distances. Moreover, toponym correction promotes the use of more diverse spatial data sources, such as Lidar, domain gazetteers, Wikimedia, and streetscapes, which are expected to usher in a new era of geo-parsing with toponym corrections.

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A reasoning model for geo-referencing named and unnamed spatial entities in natural language place descriptions

Cited by 3 publications

References 39 publications

Representing and Reasoning with Context-Sensitive Vague Place Descriptions

Representing and Reasoning with Context-Sensitive Vague Place Descriptions

Automated Interpretation of Place Descriptions: Determining Entity Types for Querying OSM

New Era for Geo-Parsing to Obtain Actual Locations: A Novel Toponym Correction Method Based on Remote Sensing Images

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