6,798 research outputs found
Recommended from our members
An Ontology for Grounding Vague Geographic Terms
Many geographic terms, such as âriverâ and âlakeâ, are vague, with no clear boundaries of application. In particular, the spatial extent of such features is often vaguely carved out of a continuously varying observable domain. We present a means of defining vague terms using standpoint semantics, a refinement of the
philosophical idea of supervaluation semantics. Such definitions can be grounded in actual data by geometric analysis and segmentation of the data set. The issues
raised by this process with regard to the nature of boundaries and domains of logical quantification are discussed. We describe a prototype implementation of a system capable of segmenting attributed polygon data into geographically significant regions and evaluating queries involving vague geographic feature terms
Recommended from our members
Automatic grounding of vague geographic ontology in data
In constructing an ontological theory of a domain such as geography, it is important not only to take account of the vagueness and ambiguity which is inherent in many of the relevant concepts, but also to be able to relate the high-level definitions of the theory to actual sets of data of varying kinds. Any attempt to ignore or remove vagueness and ambiguity risks errors and conflict in the ontological theory with the knowledge of different domain experts, while an inability to ground the theory in real data limits its practical use. We present here a means of structuring such a theory to handle these issues in a principled manner, which lends itself to concrete implementation. We illustrate with reference to several examples from the domain of hydrography
Applying spatial reasoning to topographical data with a grounded geographical ontology
Grounding an ontology upon geographical data has been pro-
posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of
points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason abou
Microtheories for SDI - Accounting for diversity of local conceptualisations at a global level
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The categorization and conceptualization of geographic features is fundamental to cartography,
geographic information retrieval, routing applications, spatial decision support
and data sharing in general. However, there is no standard conceptualization of
the world. Humans conceptualize features based on numerous factors including cultural
background, knowledge, motivation and particularly space and time. Thus, geographic
features are prone to multiple, context-dependent conceptualizations reflecting local
conditions. This creates semantic heterogeneity and undermines interoperability. Standardization
of a shared definition is often employed to overcome semantic heterogeneity.
However, this approach loses important local diversity in feature conceptualizations and
may result in feature definitions which are too broad or too specific. This work proposes
the use of microtheories in Spatial Data Infrastructures, such as INSPIRE, to account
for diversity of local conceptualizations while maintaining interoperability at a global
level. It introduces a novel method of structuring microtheories based on space and
time, represented by administrative boundaries, to reflect variations in feature conceptualization.
A bottom-up approach, based on non-standard inference, is used to create
an appropriate global-level feature definition from the local definitions. Conceptualizations
of rivers, forests and estuaries throughout Europe are used to demonstrate how
the approach can improve the INSPIRE data model and ease its adoption by European
member states
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams
Neogeography is the combination of user generated data and experiences with mapping technologies. In this article we present a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The extracted information should be integrated together to form a collective knowledge about certain domain. This structured information can be used further to help users from the same domain who want to get information using simple question answering system. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology
Automated reasoning on vague concepts using formal ontologies, with an application to event detection on video data
In this paper we lay the foundations of a formal ontology for the characterisation of vague concepts sourced from natural language, applying principles of event calculus and super-valuation semantics. We focus on a specific set of motion events and related concepts, motivated by the aim to develop an automated reasoning system able to detect occurrences of such events in video scenes. Our goal is to provide a general methodology for the formalisation of vague concepts, and to address the issue of vagueness in formal ontologies
- âŠ