TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response Jens De Bruijn1, Hans De Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, Jeroen C.J.H
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Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text
remote sensing Article Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text Edwin Aldana-Bobadilla 1,∗ , Alejandro Molina-Villegas 2 , Ivan Lopez-Arevalo 3 , Shanel Reyes-Palacios 3, Victor Muñiz-Sanchez 4 and Jean Arreola-Trapala 4 1 Conacyt-Centro de Investigación y de Estudios Avanzados del I.P.N. (Cinvestav), Victoria 87130, Mexico; [email protected] 2 Conacyt-Centro de Investigación en Ciencias de Información Geoespacial (Centrogeo), Mérida 97302, Mexico; [email protected] 3 Centro de Investigación y de Estudios Avanzados del I.P.N. Unidad Tamaulipas (Cinvestav Tamaulipas), Victoria 87130, Mexico; [email protected] (I.L.); [email protected] (S.R.) 4 Centro de Investigación en Matemáticas (Cimat), Monterrey 66628, Mexico; [email protected] (V.M.); [email protected] (J.A.) * Correspondence: [email protected] Received: 10 July 2020; Accepted: 13 September 2020; Published: 17 September 2020 Abstract: The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task could be approached through a machine learning approach, in which case a model is trained to recognize a sequence of characters (words) corresponding to geographic entities. The second task consists of assigning such entities to their most likely coordinates. Frequently, the latter process involves solving referential ambiguities. In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation. -
Knowledge-Driven Geospatial Location Resolution for Phylogeographic
Bioinformatics, 31, 2015, i348–i356 doi: 10.1093/bioinformatics/btv259 ISMB/ECCB 2015 Knowledge-driven geospatial location resolution for phylogeographic models of virus migration Davy Weissenbacher1,*, Tasnia Tahsin1, Rachel Beard1,2, Mari Figaro1,2, Robert Rivera1, Matthew Scotch1,2 and Graciela Gonzalez1 1Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and 2Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA *To whom correspondence should be addressed. Abstract Summary: Diseases caused by zoonotic viruses (viruses transmittable between humans and ani- mals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveil- lance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public data- bases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles. Motivation: In this article, we present a system for detection and disambiguation of locations (topo- nym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using inte- grated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a ‘metadata heuristic’). Results: For detecting and disambiguating locations, our system performed best using the meta- data heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). -
Geotagging: an Innovative Tool to Enhance Transparency and Supervision
Geotagging: An Innovative Tool To Enhance Transparency and Supervision Transparency through Geotagging Noel Sta. Ines Geotagging Geotagging • Process of assigning a geographical reference, i.e, geographical coordinates (latitude and longitude) + elevation - to an object. • This could be done by taking photos, nodes and tracks with recorded GPS coordinates. • This allows geo-tagged object or SP data to be easily and accurately located on a map. WhatThe is Use Geotagging and Implementation application in of the Geo Philippines?-Tagging • A revolutionary and inexpensive approach of using ICT + GPS applications for accurate visualization of sub-projects • Device required is only a GPS enabled android cellphone, and access to freely available apps • Easily replicable for mainstreaming to Government institutions & CSOs • Will help answer the question: Is the right activity implemented in the right place? – (asset verification tool) Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 1: Visualization of a farm-to-market road in a conflict area: showing specific location, ground distance, track / alignment, elevation profile, ground photos (with coordinates, date and time taken) of Farm-to-market Road, i.e. baseline information + Progress photos + 3D visualization Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 2: Location and Visualization of rehabilitation of a city road in Earthquake damaged-area in Tagbilaran, Bohol, Philippines by the auditors and the citizen volunteers Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. 3 Visualization of a water supply project showing specific location, elevation profile from the source , distribution lines and faucets, and ground photos of community faucets Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. -
Geotagging Photos to Share Field Trips with the World During the Past Few
Geotagging photos to share field trips with the world During the past few years, numerous new online tools for collaboration and community building have emerged, providing web-users with a tremendous capability to connect with and share a variety of resources. Coupled with this new technology is the ability to ‘geo-tag’ photos, i.e. give a digital photo a unique spatial location anywhere on the surface of the earth. More precisely geo-tagging is the process of adding geo-spatial identification or ‘metadata’ to various media such as websites, RSS feeds, or images. This data usually consists of latitude and longitude coordinates, though it can also include altitude and place names as well. Therefore adding geo-tags to photographs means adding details as to where as well as when they were taken. Geo-tagging didn’t really used to be an easy thing to do, but now even adding GPS data to Google Earth is fairly straightforward. The basics Creating geo-tagged images is quite straightforward and there are various types of software or websites that will help you ‘tag’ the photos (this is discussed later in the article). In essence, all you need to do is select a photo or group of photos, choose the "Place on map" command (or similar). Most programs will then prompt for an address or postcode. Alternatively a GPS device can be used to store ‘way points’ which represent coordinates of where images were taken. Some of the newest phones (Nokia N96 and i- Phone for instance) have automatic geo-tagging capabilities. These devices automatically add latitude and longitude metadata to the existing EXIF file which is already holds information about the picture such as camera, date, aperture settings etc. -
Geotag Propagation in Social Networks Based on User Trust Model
1 Geotag Propagation in Social Networks Based on User Trust Model Ivan Ivanov, Peter Vajda, Jong-Seok Lee, Lutz Goldmann, Touradj Ebrahimi Multimedia Signal Processing Group Ecole Polytechnique Federale de Lausanne, Switzerland Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 2 We introduce users in our system for geotagging in order to simulate a real social network GPS coordinates to derive geographical annotation, which are not available for the majority of web images and photos A GPS sensor in a camera provides only the location of the photographer instead of that of the captured landmark Sometimes GPS and Wi-Fi geotagging determine wrong location due to noise http: //www.pl acecas t.net Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 3 Tag – short textual annotation (free-form keyword)usedto) used to describe photo in order to provide meaningful information about it User-provided tags may sometimes be spam annotations given on purpose or wrong tags given by mistake User can be “an algorithm” http://code.google.com/p/spamcloud http://www.flickr.com/photos/scriptingnews/2229171225 Multimedia Signal Processing Group Swiss Federal Institute of Technology Goal 4 Consider user trust information derived from users’ tagging behavior for the tag propagation Build up an automatic tag propagation system in order to: Decrease the anno ta tion time, and Increase the accuracy of the system http://www.costadevault.com/blog/2010/03/listening-to-strangers Multimedia -
UC Berkeley International Conference on Giscience Short Paper Proceedings
UC Berkeley International Conference on GIScience Short Paper Proceedings Title Tweet Geolocation Error Estimation Permalink https://escholarship.org/uc/item/0wf6w9p9 Journal International Conference on GIScience Short Paper Proceedings, 1(1) Authors Holbrook, Erik Kaur, Gupreet Bond, Jared et al. Publication Date 2016 DOI 10.21433/B3110wf6w9p9 Peer reviewed eScholarship.org Powered by the California Digital Library University of California GIScience 2016 Short Paper Proceedings Tweet Geolocation Error Estimation E. Holbrook1, G. Kaur1, J. Bond1, J. Imbriani1, C. E. Grant1, and E. O. Nsoesie2 1University of Oklahoma, School of Computer Science Email: {erik; cgrant; gkaur; jared.t.bond-1; joshimbriani}@ou.edu 2University of Washington, Institute for Health Metrics and Evaluation Email: [email protected] Abstract Tweet location is important for researchers who study real-time human activity. However, few studies have examined the reliability of social media user-supplied location and description in- formation, and most who do use highly disparate measurements of accuracy. We examined the accuracy of predicting Tweet origin locations based on these features, and found an average ac- curacy of 1941 km. We created a machine learning regressor to evaluate the predictive accuracy of the textual content of these fields, and obtained an average accuracy of 256 km. In a dataset of 325788 tweets over eight days, we obtained city-level accuracy for approximately 29% of users based only on their location field. We describe a new method of measuring location accuracy. 1. Introduction With the rise of micro-blogging services and publicly available social media posts, the problem of location identification has become increasingly important. -
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data Michael D. Lieberman, Hanan Samet, Jagan Sankaranarayanan Center for Automation Research, Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland College Park, MD 20742, USA {codepoet, hjs, jagan}@cs.umd.edu Abstract— The successful execution of location-based and feature-based queries on spatial databases requires the construc- tion of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience’s vocabulary of geographic locations, termed its Fig. 1. Locations mentioned in news articles about the May 2009 swine flu pandemic, obtained by geotagging related news articles. Large red circles spatial lexicon. In contrast to previous work, a new spatial lexicon indicate high frequency, and small circles are color coded according to recency, model is presented that distinguishes between a global lexicon of with lighter colors indicating the newest mentions. locations known to all audiences, and an audience-specific local lexicon. -
Geotagging Tweets Using Their Content
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference Geotagging Tweets Using Their Content Sharon Paradesi Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology [email protected] Abstract Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audi- ence based on its geographic location is a major chal- lenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a per- formance improvement of three orders by TwitterTag- ger compared to that of the baseline model. Introduction People use popular social networking websites such as Face- book and Twitter to share their interests and opinions with their friends and the online community. Harnessing this in- formation in real-time and showing it to the relevant audi- ence based on its geographic location is a major challenge. Figure 1: Architecture of TwitterTagger The microblogging social medium, Twitter, is used because of its relevance to users in real-time. The goal of this research is to identify the locations refer- Pipe POS tagger1. The noun phrases are then compared with enced in a tweet and show relevant tweets to a user based on the USGS database2 of locations. Common noun phrases, that user’s location. For example, a user traveling to a new such as ‘Love’ and ‘Need’, are also place names and would place would would not necessarily know all the events hap- be geotagged. To avoid this, the system uses a greedy ap- pening in that place unless they appear in the mainstream proach of phrase chunking. -
Structured Toponym Resolution Using Combined Hierarchical Place Categories∗
In GIR’13: Proceedings of the 7th ACM SIGSPATIAL Workshop on Geographic Information Retrieval, Orlando, FL, November 2013 (GIR’13 Best Paper Award). Structured Toponym Resolution Using Combined Hierarchical Place Categories∗ Marco D. Adelfio Hanan Samet Center for Automation Research, Institute for Advanced Computer Studies Department of Computer Science, University of Maryland College Park, MD 20742 USA {marco, hjs}@cs.umd.edu ABSTRACT list or table is geotagged with the locations it references. To- ponym resolution and geotagging are common topics in cur- Determining geographic interpretations for place names, or rent research but the results can be inaccurate when place toponyms, involves resolving multiple types of ambiguity. names are not well-specified (that is, when place names are Place names commonly occur within lists and data tables, not followed by a geographic container, such as a country, whose authors frequently omit qualifications (such as city state, or province name). Our aim is to utilize the context of or state containers) for place names because they expect other places named within the table or list to disambiguate the meaning of individual place names to be obvious from place names that have multiple geographic interpretations. context. We present a novel technique for place name dis- ambiguation (also known as toponym resolution) that uses Geographic references are a very common component of Bayesian inference to assign categories to lists or tables data tables that can occur in both (1) the case where the containing place names, and then interprets individual to- primary entities of a table are geographic or (2) the case ponyms based on the most likely category assignments. -
An Introduction to Georss: a Standards Based Approach for Geo-Enabling RSS Feeds
Open Geospatial Consortium Inc. Date: 2006-07-19 Reference number of this document: OGC 06-050r3 Version: 1.0.0 Category: OpenGIS® White Paper Editors: Carl Reed OGC White Paper An Introduction to GeoRSS: A Standards Based Approach for Geo-enabling RSS feeds. Warning This document is not an OGC Standard. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting do Document type: OpenGIS® White Paper Document subtype: White Paper Document stage: APPROVED Document language: English OGC 06-050r3 Contents Page i. Preface – Executive Summary........................................................................................ iv ii. Submitting organizations............................................................................................... iv iii. GeoRSS White Paper and OGC contact points............................................................ iv iv. Future work.....................................................................................................................v Foreword........................................................................................................................... vi Introduction...................................................................................................................... vii 1 Scope.................................................................................................................................1 -
Toponym Resolution in Scientific Papers
SemEval-2019 Task 12: Toponym Resolution in Scientific Papers Davy Weissenbachery, Arjun Maggez, Karen O’Connory, Matthew Scotchz, Graciela Gonzalez-Hernandezy yDBEI, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA zBiodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ 85281, USA yfdweissen, karoc, [email protected] zfamaggera, [email protected] Abstract Disambiguation of toponyms is a more recent task (Leidner, 2007). We present the SemEval-2019 Task 12 which With the growth of the internet, the public adop- focuses on toponym resolution in scientific ar- ticles. Given an article from PubMed, the tion of smartphones equipped with Geographic In- task consists of detecting mentions of names formation Systems and the collaborative devel- of places, or toponyms, and mapping the opment of comprehensive maps and geographi- mentions to their corresponding entries in cal databases, toponym resolution has seen an im- GeoNames.org, a database of geospatial loca- portant gain of interest in the last two decades. tions. We proposed three subtasks. In Sub- Not only academic but also commercial and open task 1, we asked participants to detect all to- source toponym resolvers are now available. How- ponyms in an article. In Subtask 2, given to- ponym mentions as input, we asked partici- ever, their performance varies greatly when ap- pants to disambiguate them by linking them plied on corpora of different genres and domains to entries in GeoNames. In Subtask 3, we (Gritta et al., 2018). Toponym disambiguation asked participants to perform both the de- tackles ambiguities between different toponyms, tection and the disambiguation steps for all like Manchester, NH, USA vs. -
From RDF to RSS and Atom
From RDF to RSS and Atom: Content Syndication with Linked Data Alex Stolz Martin Hepp Universität der Bundeswehr München Universität der Bundeswehr München E-Business and Web Science Research Group E-Business and Web Science Research Group 85577 Neubiberg, Germany 85577 Neubiberg, Germany +49-89-6004-4277 +49-89-6004-4217 [email protected] [email protected] ABSTRACT about places, artists, brands, geography, transportation, and related facts. While there are still quality issues in many sources, For typical Web developers, it is complicated to integrate content there is little doubt that much of the data could add some value from the Semantic Web to an existing Web site. On the contrary, when integrated into external Web sites. For example, think of a most software packages for blogs, content management, and shop Beatles fan page that wants to display the most recent offers for applications support the simple syndication of content from Beatles-related products for less than 10 dollars, a hotel that wants external sources via data feed formats, namely RSS and Atom. In to display store and opening hours information about the this paper, we describe a novel technique for consuming useful neighborhood on its Web page, or a shopping site enriched by data from the Semantic Web in the form of RSS or Atom feeds. external reviews or feature information related to a particular Our approach combines (1) the simplicity and broad tooling product. support of existing feed formats, (2) the precision of queries Unfortunately, consuming content from the growing amount of against structured data built upon common Web vocabularies like Semantic Web data is burdensome (if not prohibitively difficult) schema.org, GoodRelations, FOAF, SIOC, or VCard, and (3) the for average Web developers and site owners, for several reasons: ease of integrating content from a large number of Web sites and Even in the ideal case of having a single SPARQL endpoint [2] at other data sources of RDF in general.