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 Protégé 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

Approche ontologique pour la modélisation de carte contextuelle

This paper first presents a state of the art of context modeling approaches in an information system. We then propose an approach based on a spatial-temporal ontology to automate the data selection step in the process of making personalized maps. The proposed approach of context modeling takes into consideration the user’s profile and preferences, as well as events related to an environment. We then aim, by logical reasoning, to be able to deduce a selection of categories and objects that will contribute to the creation of a personalized map

Knowledge-Based Recommendation for On-Demand Mapping: Application to Nautical Charts

Maps have long been seen as a single cartographic product for different uses, with the user having to adapt their interpretation to his or her own needs. On-demand mapping reverses this paradigm in that it is the map that adapts to the user’s needs and context of use. Still often manual and reserved for professionals, on-demand mapping is evolving toward an automation of its processes and a democratization of its use. An on-demand mapping service is a chain of several consecutive steps leading to a target map that precisely meets the needs and requirements of a user. This article addresses the issue of selecting relevant thematic layers with a specific context of use. We propose a knowledge-based recommendation approach that aims to guide a cartographer through the process of map-making. Our system is based on high- and low-level ontologies, the latter modeling the concepts specific to different types of maps targeted. By focusing on maritime maps, we address the representation of knowledge in this context of use, where recommendations rely on axiomatic and rule-based reasoning. For this purpose, we choose description logics as a formalism for knowledge representation in order to make cartographic knowledge machine readable

Automatic Nested Spatial Entity and Spatial Relation Extraction From Text for Knowledge Graph Creation: A Baseline Approach and a Benchmark Dataset

International audienceAutomatically extracting geographic information from text is the key to harnessing the vast amount of spatial knowledge that only exists in this unstructured form. The fundamental elements of spatial knowledge include spatial entities, their types and the spatial relations between them. Structuring the spatial knowledge contained within text as a geospatial knowledge graph, and disambiguating the spatial entities, significantly facilitates its reuse. The automatic extraction of geographic information from text also allows the creation or enrichment of gazetteers. We propose a baseline approach for nested spatial entity and binary spatial relation extraction from text, a new annotated French-language benchmark dataset on the maritime domain that can be used to train algorithms for both extraction tasks, and benchmark results for the two tasks carried out individually and end-to-end. Our approach involves applying the Princeton University Relation Extraction system (PURE), made for flat, generic entity extraction and generic binary relation extraction, to the extraction of nested, spatial entities and spatial binary relations. By extracting nested spatial entities and the spatial relations between them, we have more information to aid entity disambiguation. In our experiments we compare the performance of a pretrained monolingual French BERT language model with that of a pretrained multilingual BERT language model, and study the effect of including cross-sentence context. Our results reveal very similar results for both models, although the multilingual model performs slightly better in entity extraction, and the monolingual model has slightly better relation extraction and end-to-end perfor- mances. We observe that increasing the amount of cross-sentence context improves the results for entity extraction whereas it has the opposite effect on relation extraction

1D and 3D modelling of PCMI during a RIA with ALCYONE v1.1

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