CRII: III: RI: Empowering Multi-Conceptual Spatial Reasoning with a Repository of Qualitative and Quantitative Spatial Ontologies

CRII：III：RI：通过定性和定量空间本体存储库增强多概念空间推理

基本信息

批准号：
1565811
负责人：
Torsten Hahmann
金额：
$ 17.5万
依托单位：
University of Maine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565811&HistoricalAwards=false
关键词：
CRII III RI Empowering Multi

项目摘要

People have manifold ways to express and process spatial information. As needed, they employ conceptualizations of space, so-called cognitive maps, that differ in granularity, scope, and precision. Based on the context, people use different implicit assumptions to interpret a spatial relation such as "A is contained in B." The assumptions made or implied by different conceptualizations often even contradict another (e.g., in "the lake contains a bay" the bay is a subregion of the lake while in "the lake contains an island" the island is surrounded by the lake). This is not an issue for people: we are able to quickly and reliably decide which conceptualization is most suitable in a specific situation, often choosing the simplest applicable conceptualization. For instance, people can quickly make navigation decisions ("Do I need to travel north or south on the Interstate?") or answer simple spatial queries ("Is the ocean east or west from here?"). Similar capabilities for flexibly utilizing multiple conceptualizations would make computational tools for recording and processing spatial information much more powerful and user-friendly. Towards this goal, the proposed research will investigate a formalism and basic procedures for automatically choosing a spatial representation best suited to solve a specific task, such as finding data with certain qualitative characteristics, answering a spatial query, or testing a spatial hypothesis. The research will contribute to a theoretical foundation for collecting, accessing, and manipulating spatial information in more natural ways without impeding its efficient processing in information systems. It will lower the barrier of entry for interacting with and analyzing spatial information and promotes technologies to cut time and costs typically spent on transforming diverse spatial data sets into a coherent model.In this project, the different spatial conceptualizations will be encoded as machine-interpretable spatial ontologies and placed in a structured ontology repository that leverages relationships from mathematical logic. The formal foundation for comparing the expressivity of ontologies using the mathematical notion of definability will be developed and used to organize the repository by differences in ontological assumptions and expressivity. This structure will be exploited by procedures for automatically selecting an ontology that best fits a specific spatial task. In addition, formal encodings of the knowledge necessary to convert spatial information from one ontology to another will be investigated, accompanied by procedures that utilize this information for automatically identifying and converting pieces of geometric background knowledge relevant to a specific spatial task. The research in this project will contribute to a better understanding of how logical relationships can be utilized to formally compare the expressiveness of two spatial ontologies, and how a spatial ontology repository can be supplemented by information that allows automated conversion of knowledge based on different ontologies. For further information see the project web page: http://www.spatial.maine.edu/~torsten/projects/QQSOR.shtml

人们有表达和处理空间信息的多种方式。根据需要，它们采用了空间的概念化，所谓的认知图，在粒度，范围和精度方面有所不同。基于上下文，人们使用不同的隐式假设来解释空间关系，例如“ A中包含B”。不同概念化做出或暗示的假设通常与另一个概念相矛盾（例如，在“湖中包含一个海湾”中，海湾是湖的一个子区域，而在“湖泊包含一个岛屿”中，该岛被湖泊包围）。这不是人们的问题：我们能够快速，可靠地决定哪种概念化最适合在特定情况下，通常选择最简单的适用概念化。例如，人们可以迅速做出导航决定（“我需要在州际公路上向北或向南行驶？”）或回答简单的空间查询（“海洋是从这里或西部的海洋？”）。灵活利用多个概念化的类似功能会使计算工具录制和处理空间信息更强大和用户友好。为了实现这一目标，拟议的研究将研究形式主义和基本程序，以自动选择最适合解决特定任务的空间表示，例如查找具有某些定性特征的数据，回答空间查询或测试空间假设。这项研究将为以更自然的方式收集，访问和操纵空间信息的理论基础，而不会阻碍其在信息系统中的有效处理。它将降低与空间信息进行交互和分析的进入的障碍，并促进技术以削减时间和成本通常用于将多样化的空间数据集转换为相干模型的时间。在该项目中，不同的空间概念化将以机器解释为机器解释的空间交流，并将其放在结构上的功能上，并将其放置在依据上的效率上。将开发并使用使用数学性概念来比较本体论的表达性的正式基础，并将其用于通过本体论假设和表现力的差异来组织存储库。该结构将通过自动选择最适合特定空间任务的本体来利用这种结构。此外，将研究将空间信息从一个本体论转换为另一种本体学所必需的知识的正式编码，并附有利用此信息自动识别和转换与特定空间任务相关的几何背景知识的程序。该项目中的研究将有助于更好地理解如何利用逻辑关系正式比较两个空间本体论的表现力，以及如何通过信息来补充空间本体论点存储库，该信息允许允许基于不同本体论的知识自动转换。有关更多信息，请参见项目网页：http：//www.spatial.maine.edu/~torsten/projects/qqsor.shtml

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Model-Finding for Externally Verifying FOL Ontologies: A Study of Spatial Ontologies

外部验证 FOL 本体的模型寻找：空间本体的研究

DOI：
10.3233/faia200675
发表时间：
2020
期刊：
Proc. of the International Conference on Formal Ontology in Information System (FOIS-2020
影响因子：
0
作者：
Stephen, S.;Hahmann, T.
通讯作者：
Hahmann, T.

Automatically Extracting OWL Versions of FOL Ontologies

自动提取 FOL 本体的 OWL 版本