Context and Vagueness in Automated Interpretation of Place Description: A Computational Model

Wolter, Diedrich; Yousaf, Madiha

doi:10.1007/978-3-319-63946-8_27

Cited by 7 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Locality descriptions are complex, either implicitly or explicitly [27]. From the cognitive psychology perspective, when the RO lacks a related quantitative distance, the RO tends to be near the human [28], [48]. For this situation, we consider the near relation as a potential location clue to assist in positioning.…”

Section: Definition 3 Stolen-region: the Stolen-region Refers To Thementioning

confidence: 99%

Indoors Positioning Based on Spatial Relationships in Locality Description

Wang

Xiu²,

Fan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Spatial relationships exist in our daily communication and provide many locality clues, but minimal attention has been devoted to positioning localities indoors with spatial relationships extracted from locality descriptions. Locality description generally contains spatial relationships (i.e., topological, distance, and direction relations) and reference objects (ROs). For locality description positioning, distance and direction relation convey more clue than topology and combined together can provide much clue to positioning. Explicit and implicit features are inherent characteristics of locality descriptions. Based on the analysis of locality descriptions, a classification scheme of indoor locality descriptions is provided. To address the positioning locality with spatial relationships, we propose a two-state method. First, the located region is obtained according to the fuzzy region of spatial relations in locality description. Second, the probability distribution is determined, which is a joint probability function that consists of relative direction and distance (quantitative or qualitative distance) membership functions. The relative direction membership function is conducted based on human's direction cognition is related to angular information. The trapezoid quantitative distance membership function is based on indoor cognitive experiment. The qualitative distance membership function for indoor ROs is based on minimum Euclidean distance and stolen area. From the cognition and computation perspectives, the visible boundary met some restrictions is provided and a sampling method is defined. To obtain the unique positioning locality, a principle is proposed. We evaluate our method by conducting an indoor cognitive experiment and demonstrate that a positioning accuracy of 3.50 m can be achieved with semantically derived spatial relationships.

show abstract

Section: Definition 3 Stolen-region: the Stolen-region Refers To Thementioning

confidence: 99%

Indoors Positioning Based on Spatial Relationships in Locality Description

Wang

Xiu²,

Fan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Most of them provide contextual knowledge, which could affect the interpretation of other information communicated in place descriptions (e.g., spatial relations), and, thus, should be captured. The definition of context is task-specific; in this research we adopt the categorization proposed by Wolter and Yousaf [68] of description-, environment-, and human-dependent contexts. For instance, near can refer to different distances according to other places relevant to the discourse [21] (description-dependent context).…”

Section: Information Not Captured In the Original Place Graph Modelmentioning

confidence: 99%

“…Individuals may describe the same environment differently, in terms of the selection of place and place semantics, spatial relations, reference frame, and conceptualization. The intention of giving a description to a recipient (or recipients) may also influence how a description will be organized [68]. Human-level factors such as age, gender, ethnicity, and degree of familiarity with the environment have also been identified as being influential on the meaning of the spatial relationships communicated [79].…”

Section: Description Context and Source Contextmentioning

confidence: 99%

A Graph Database Model for Knowledge Extracted from Place Descriptions

Chen

Vasardani

Winter

et al. 2018

IJGI

View full text Add to dashboard Cite

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modelling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using a graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

show abstract

“…Most of them provide contextual knowledge, which could affect the interpretation of other information communicated in place descriptions (e.g., spatial relations), and, thus, should be captured. The definition of context is task-specific; in this research we adopt the categorization proposed by Wolter and Yousaf [52] of description-, environment-, and human-dependent contexts, as shown in Figure 3. For instance, near can refer to different distances according to other places relevant to the discourse [19] (description-dependent context).…”

Section: Information Not Captured In the Original Place Graph Modelmentioning

confidence: 99%

“…Individuals may describe the same environment differently, in terms of the selection of place and place semantics, spatial relations, reference frame, and conceptualization. The intention of giving a description to a recipient (or recipients) may also influence how a description will be organized [52]. Human-level factors such as age, gender, ethnicity, and degree of familiarity with the environment have also been identified as being influential on the meaning of the spatial relationships communicated [64].…”

Section: Description Context and Source Contextmentioning

confidence: 99%

A Graph Database Model for Knowledge Extracted from Place Descriptions

Chen¹,

Vasardani²,

Winter³

et al. 2018

Preprint

View full text Add to dashboard Cite

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modeling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using the Neo4j graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

show abstract

Context and Vagueness in Automated Interpretation of Place Description: A Computational Model

Cited by 7 publications

References 4 publications

Indoors Positioning Based on Spatial Relationships in Locality Description

Indoors Positioning Based on Spatial Relationships in Locality Description

A Graph Database Model for Knowledge Extracted from Place Descriptions

A Graph Database Model for Knowledge Extracted from Place Descriptions

Contact Info

Product

Resources

About