An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance

While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on. Place names have long been studied by geographers, e.g., to understand their origins and relations to family names. However, there is a lack of large-scale empirical studies that examine the localness of place names and their changes with geographic distance. In addition to enhancing our understanding of the coherence of geographic regions, such empirical studies are also significant for geographic information retrieval where they can inform computational models and improve the accuracy of place name disambiguation. In this work, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types, which is highly consistent among different geographic regions. We also examine the decaying effect of POI name similarity with the increase of distance among POIs. While our analysis focuses on urban POI names, the presented methods can be generalized to other place types as well, such as mountain peaks and streets

Automatic reconstruction of itineraries from descriptive texts

Esta tesis se inscribe dentro del marco del proyecto PERDIDO donde los objetivos son la extracción y reconstrucción de itinerarios a partir de documentos textuales. Este trabajo se ha realizado en colaboración entre el laboratorio LIUPPA de l' Université de Pau et des Pays de l' Adour (France), el grupo de Sistemas de Información Avanzados (IAAA) de la Universidad de Zaragoza y el laboratorio COGIT de l' IGN (France). El objetivo de esta tesis es concebir un sistema automático que permita extraer, a partir de guías de viaje o descripciones de itinerarios, los desplazamientos, además de representarlos sobre un mapa. Se propone una aproximación para la representación automática de itinerarios descritos en lenguaje natural. Nuestra propuesta se divide en dos tareas principales. La primera pretende identificar y extraer de los textos describiendo itinerarios información como entidades espaciales y expresiones de desplazamiento o percepción. El objetivo de la segunda tarea es la reconstrucción del itinerario. Nuestra propuesta combina información local extraída gracias al procesamiento del lenguaje natural con datos extraídos de fuentes geográficas externas (por ejemplo, gazetteers). La etapa de anotación de informaciones espaciales se realiza mediante una aproximación que combina el etiquetado morfo-sintáctico y los patrones léxico-sintácticos (cascada de transductores) con el fin de anotar entidades nombradas espaciales y expresiones de desplazamiento y percepción. Una primera contribución a la primera tarea es la desambiguación de topónimos, que es un problema todavía mal resuelto dentro del reconocimiento de entidades nombradas (Named Entity Recognition - NER) y esencial en la recuperación de información geográfica. Se plantea un algoritmo no supervisado de georreferenciación basado en una técnica de clustering capaz de proponer una solución para desambiguar los topónimos los topónimos encontrados en recursos geográficos externos, y al mismo tiempo, la localización de topónimos no referenciados. Se propone un modelo de grafo genérico para la reconstrucción automática de itinerarios, donde cada nodo representa un lugar y cada arista representa un camino enlazando dos lugares. La originalidad de nuestro modelo es que además de tener en cuenta los elementos habituales (caminos y puntos del recorrido), permite representar otros elementos involucrados en la descripción de un itinerario, como por ejemplo los puntos de referencia visual. Se calcula de un árbol de recubrimiento mínimo a partir de un grafo ponderado para obtener automáticamente un itinerario bajo la forma de un grafo. Cada arista del grafo inicial se pondera mediante un método de análisis multicriterio que combina criterios cualitativos y cuantitativos. El valor de estos criterios se determina a partir de informaciones extraídas del texto e informaciones provenientes de recursos geográficos externos. Por ejemplo, se combinan las informaciones generadas por el procesamiento del lenguaje natural como las relaciones espaciales describiendo una orientación (ej: dirigirse hacia el sur) con las coordenadas geográficas de lugares encontrados dentro de los recursos para determinar el valor del criterio ``relación espacial''. Además, a partir de la definición del concepto de itinerario y de las informaciones utilizadas en la lengua para describir un itinerario, se ha modelado un lenguaje de anotación de información espacial adaptado a la descripción de desplazamientos, apoyándonos en las recomendaciones del consorcio TEI (Text Encoding and Interchange). Finalmente, se ha implementado y evaluado las diferentes etapas de nuestra aproximación sobre un corpus multilingüe de descripciones de senderos y excursiones (francés, español, italiano)

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Enriching the Digital Library Experience: Innovations With Named Entity Recognition and Geographic Information System Technologies

Digital libraries are seeking innovative ways to share their resources and enhance user experience. To this end, numerous openly available technologies can be exploited. For this project, NER technology was applied to a subset of the Documenting the American South (DocSouth) digital collections. Personal and location names were hand-annotated to achieve a gold standard, and GATE, a text engineering tool, was run under two conditions: a defaults baseline and a test run that included gazetteers built from DocSouth's Colonial and State Records collection. Overall, GATE performance is promising, and numerous strategies for improvement are discussed. Next, derived location annotations were georeferenced and stored in a geodatabase through automated processes, and a prototype for a web-based map search was developed using the Google Maps API. This project showcases innovations with automated NER coupled with GIS technologies, and strongly supports further investment in applying these techniques across DocSouth and other digital libraries

A study of the tourism web coverage in Switzerland

This paper discusses experiments that were performed to understand the geographic and linguistic coverage of web resources focusing on tourism-related themes in Switzerland. The research was prompted by the observation that studies in geographic information retrieval (GIR) and volunteered geographic information (VGI) commonly assume web coverage to be homogenous across geographic space, themes, and languages. There are, however, strong hints that this assumption is unfounded (Pasley et al. 2008). The goal of studying the geographic web coverage is one of the preliminary steps in generating (geographic) data from the web that can be used as valid information. An idea on how well certain areas are geographically covered by information available on the web, their frequency and patterns that emerge from this data collection help in the decision of preselecting web data for further investigation. For this experiment the language is also considered as coverage varies greatly on the tongue of the place. Ad hoc tourism information is readily available on the web in the form of pages that contain news, lists, catalogue, reviews, blogs and multimedia content. All this provides a vast playground for tourism as a use case for generating geographic information from the web. The key questions driving this research are: 1) What is the geographic distribution of web coverage for tourism-related themes? 2) How does language affect web coverage

Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding

Place name disambiguation, i.e., toponym disambiguation or toponym resolution, is the task of correctly identifying a place from a set of places sharing a common name. It contributes to a variety of tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here I propose a novel approach to the disambiguation of place names from short texts that integrates three models: entity co-occurrence, topic modeling, and word embedding. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. The third model uses word embeddings to uncover the semantic relatedness between places and contexts. I evaluate this approach using a corpus of short texts collected through web scraping, determine the suitable weights for the models, and demonstrate that the combined model, i.e., Things and Strings Model, outperforms benchmark systems such as DBpedia Spotlight, TextRazor, and Open Calais by up to 85% in F-score and 46% in Precision at 1. A web service is built to demonstrate the proposed method and it can be a building block for those applications that need place name recognition and disambiguation