In this paper, we propose and discuss a methodology to map the spatial fingerprints of novels and authors based on all of the named urban roads (i.e., odonyms) extracted from novels. We present several ways to explore Parisian space and fictional landscapes by interactively and simultaneously browsing geographical space and literary text. Our project involves building a platform capable of retrieving, mapping and analyzing the occurrences of named urban roads in novels in which the action occurs wholly or partly in Paris. This platform will be used in several areas, such as cultural tourism, urban research, and literary analysis. The paper focuses on extracting named urban roads and mapping the results for a sample of 31 novels published between 1800 and 1914. Two approaches to the annotation of odonyms are compared. First, we describe a proof of concept using queries made via the TXM textual analysis platform. Then, we describe an automatic process using a natural language processing (NLP) method. Additionally, we mention how the geosemantic information annotated from the text (e.g., a structure combining verbs, spatial relations, named entities, adjectives and adverbs) can be used to automatically characterize the semantic content associated with named urban roads.

This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process.

L’annotation sémantique d’information spatiale a pour objectif de repérer des mots ou des syntagmes décrivant des références géographiques (noms de lieux) ainsi que diverses expressions spatiales associées. L’une des plus importantes difficultés pour concevoir un système automatique d’annotation d’un tel type d’information est due aux ambiguïtés liées aux entités spatiales. Une approche modulaire basée sur des services Web a été choisie. La méthodologie proposée repose sur la combinaison d’une étape de prétraitement (analyse morphosyntaxique), d’une cascade de transducteurs, et d’une étape de classification utilisant des ressources du Web des données. Un avantage de cette approche est la possibilité d’obtenir des traitements partiels ou encore de mettre en concurrence certains modules réalisant la même tâche. The semantic annotation of spatial information aims to identify words or phrases describing geographical references (place names) as well as various associated spatial expressions. One of the major difficulties in designing an automatic annotation system for such information is due to ambiguities related to spatial entities. A modular approach based on Web services was chosen. The proposed methodology is based on the combination of a pre-processing step using external morphosyntactic analysers, a cascade of transducers, and a classification step using linked data. An advantage of this approach is the possibility to obtain partial processing or to evaluate competing methods doing the same task.