MANSION-GS: seMANtics as the n-th dimenSION for Geographic Space

The extended understanding of geographic ecosystems, including the physical and logical description of space with associated data and activities as well as the dynamics inside, poses complex scenarios that cannot be obtained from a simple geographic-oriented data model. The main purpose of this current work is the conceptual integration of a physical space model with dynamic logic support able to describe the relations amongst the different elements composing the space as well as the relations between spaces and external elements. In the context of this work, semantics have the critical and central role of connecting and relating the different dimensions on the space, even though they are mostly a virtual dimension in the overall model

Geo-Semantic Labelling of Open Data. SEMANTiCS 2018-14th International Conference on Semantic Systems

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/

An ontology for the generalisation of the bathymetry on nautical charts

On nautical charts, undersea features are portrayed by sets of soundings (depth points) and isobaths (depth contours) from which map readers can interpret landforms. Different techniques were developed for automatic soundings selection and isobath generalisation from a sounding set. These methods are mainly used to generate a new chart from the bathymetric database or from a large scale chart through selection and simplification however a part of the process consists in selecting and emphasising undersea features on the chart according to their relevance to navigation. Its automation requires classification of the features from the set of isobaths and soundings and their generalisation through the selection and application of a set of operators according not only to geometrical constraints but also to semantic constraints. The objective of this paper is to define an ontology formalising undersea feature representation and the generalisation process achieving this representation on a nautical chart. The ontology is built in two parts addressing on one hand the definition of the features and on the other hand their generalisation. The central concept is the undersea feature around which other concepts are organised. The generalisation process is driven by the features where the objective is to select or emphasise information according to their meaning for a specific purpose. The ontologies were developed in Proteg´ e and a bathymetric database server integrating the ontology was ´ implemented. A generalisation platform was also developed and examples of representations obtained by the platform are presented. Finally, current results and on-going research are discussed

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

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest, and lake, enabling the communi- cation of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. De- spite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often in- commensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the nego- tiation: (i) ontology, (ii) cartography, (iii) culture and language, (iv) lexical definitions, (v) granularity, and (vi) semantic overload and duplication

Astronautics

Many people have had and still have misconceptions about the basic principle of rocket propulsion. Here is a comment of an unknown editorial writer of the renowned New York Times from January 13, 1920, about the pioneer of US astronautics, Robert Goddard, who at that time was carrying out the ?rst experiments with liquid propulsion engines: Professor Goddard … does not know the relation of action to reaction, and of the need to have something better than a vacuum against which to react – to say that would be absurd. Of course he only seems to lack the knowledge ladled out daily in high schools

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/ intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology

Science Information Systems Newsletter, issue 28

The purpose of the Information Systems Newsletter is to inform the space science and applications research community about information systems development and to promote coordination and collaboration by providing a forum for communication. This quarterly publication focuses on programs sponsored by the Information Systems Branch in support of NASA's Office of Space Science. Articles of interest for other programs and agencies are presented as well. The April 1993 issue is presented