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

Geocoding location expressions in Twitter messages: A preference learning method

Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors

Surveying GeoNames Gazetteer Data for the Nordic Countries

This paper takes a look at freely available gazetteer data for the Nordic countries. We examine locations in this region to understand their characteristics and the quality of the available data. Several indicators are developed and discussed to estimate the expected data quality. The distribution and coverage of the data is mapped and the accuracy and quality indicators are visualized. The used method focuses on populated places as locations of interest but can be extended to arbitrary types of locations. The results give insights into the distribution of issues based on multiple indicators and give an estimate of per-country data quality

A Coherent Unsupervised Model for Toponym Resolution

Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content. However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task. In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data. We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing. Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution. Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques. We also examine three commercial products including Reuters OpenCalais, Yahoo! YQL Placemaker, and Google Cloud Natural Language API. The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.Comment: 9 pages (+1 page reference), WWW '18 Proceedings of the 2018 World Wide Web Conferenc

Placenames analysis in historical texts: tools, risks and side effects

International audienceThis article presents an approach combining linguistic analysis, geographic information retrieval and visualization in order to go from toponym extraction in historical texts to projection on customizable maps. The toolkit is released under an open source license, it features bootstrapping options, geocod-ing and disambiguation algorithms, as well as cartographic processing. The software setting is designed to be adaptable to various historical contexts, it can be extended by further automatically processed or user-curated gazetteers, used directly on texts or plugged-in on a larger processing pipeline. I provide an example of the issues raised by generic extraction and show the benefits of integrated knowledge-based approach, data cleaning and filtering

Suomenkielisen geojäsentimen kehittäminen: kuinka hankkia sijaintitietoa jäsentelemättömistä tekstiaineistoista

Alati enemmän aineistoa tuotetaan ja jaetaan internetin kautta. Aineistot ovat vaihtelevia muodoiltaan, kuten verkkoartikkelien ja sosiaalisen media julkaisujen kaltaiset digitaaliset tekstit, ja niillä on usein spatiaalinen ulottuvuus. Teksteissä geospatiaalisuutta ilmaistaan paikannimien kautta, mutta tavanomaisilla paikkatietomenetelmillä ei kyetä käsittelemään tietoa epätäsmällisessä kielellisessä asussaan. Tämä on luonut tarpeen muuntaa tekstimuotoisen sijaintitiedon näkyvään muotoon, koordinaateiksi. Ongelmaa ratkaisemaan on kehitetty geojäsentimiä, jotka tunnistavat ja paikantavat paikannimet vapaista teksteistä, ja jotka oikein toimiessaan voisivat toimia paikkatiedon lähteenä maantieteellisessä tutkimuksessa. Geojäsentämistä onkin sovellettu katastrofihallinnasta kirjallisuudentutkimukseen. Merkittävässä osassa geojäsentämisen tutkimusta tutkimusaineiston kielenä on ollut englanti ja geojäsentimetkin ovat kielikohtaisia – tämä jättää pimentoon paitsi geojäsentimien kehitykseen vaikuttavat havainnot pienemmistä kielistä myös kyseisten kielten puhujien näkemykset. Maisterintutkielmassani pyrin vastaamaan kolmeen tutkimuskysymykseen: Mitkä ovat edistyneimmät geojäsentämismenetelmät? Mitkä kielelliset ja maantieteelliset monitulkintaisuudet vaikeuttavat tämän monitahoisen ongelman ratkaisua? Ja miten arvioida geojäsentimien luotettavuutta ja käytettävyyttä? Tutkielman soveltavassa osuudessa esittelen Fingerin, geojäsentimen suomen kielelle, ja kuvaan sen kehitystä sekä suorituskyvyn arviointia. Arviointia varten loin kaksi testiaineistoa, joista toinen koostuu Twitter-julkaisuista ja toinen uutisartikkeleista. Finger-geojäsennin, testiaineistot ja relevantit ohjelmakoodit jaetaan avoimesti. Geojäsentäminen voidaan jakaa kahteen alitehtävään: paikannimien tunnistamiseen tekstivirrasta ja paikannimien ratkaisemiseen oikeaan koordinaattipisteeseen mahdollisesti useasta kandidaatista. Molemmissa vaiheissa uusimmat metodit nojaavat syväoppimismalleihin ja -menetelmiin, joiden syötteinä ovat sanaupotusten kaltaiset vektorit. Geojäsentimien suoriutumista testataan aineistoilla, joissa paikannimet ja niiden koordinaatit tiedetään. Mittatikkuna tunnistamisessa on vastaavuus ja ratkaisemisessa etäisyys oikeasta sijainnista. Finger käyttää paikannimitunnistinta, joka hyödyntää suomenkielistä BERT-kielimallia, ja suoraviivaista tietokantahakua paikannimien ratkaisemiseen. Ohjelmisto tuottaa taulukkomuotoiseksi jäsenneltyä paikkatietoa, joka sisältää syötetekstit ja niistä mahdollisesti tunnistetut paikannimet koordinaattisijainteineen. Testiaineistot eroavat aihepiireiltään, mutta Finger suoriutuu niillä likipitäen samoin, ja suoriutuu englanninkielisillä aineistoilla tehtyihin arviointeihin suhteutettuna kelvollisesti. Virheanalyysi paljastaa useita virhelähteitä, jotka johtuvat kielten tai maantieteellisen todellisuuden luontaisesta epäselvyydestä tai ovat prosessoinnin aiheuttamia, kuten perusmuotoistamisvirheet. Kaikkia osia Fingerissä voidaan parantaa, muun muassa kehittämällä kielellistä käsittelyä pidemmälle ja luomalla kattavampia testiaineistoja. Samoin tulevaisuuden geojäsentimien tulee kyetä käsittelemään monimutkaisempia kielellisiä ja maantieteellisiä kuvaustapoja kuin pelkät paikannimet ja koordinaattipisteet. Finger ei nykymuodossaan tuota valmista paikkatietoa, jota kannattaisi kritiikittä käyttää. Se on kuitenkin lupaava ensiaskel suomen kielen geojäsentimille ja astinlauta vastaisuuden soveltavalle tutkimukselle.Ever more data is available and shared through the internet. The big data masses often have a spatial dimension and can take many forms, one of which are digital texts, such as articles or social media posts. The geospatial links in these texts are made through place names, also called toponyms, but traditional GIS methods are unable to deal with the fuzzy linguistic information. This creates the need to transform the linguistic location information to an explicit coordinate form. Several geoparsers have been developed to recognize and locate toponyms in free-form texts: the task of these systems is to be a reliable source of location information. Geoparsers have been applied to topics ranging from disaster management to literary studies. Major language of study in geoparser research has been English and geoparsers tend to be language-specific, which threatens to leave the experiences provided by studying and expressed in smaller languages unexplored. This thesis seeks to answer three research questions related to geoparsing: What are the most advanced geoparsing methods? What linguistic and geographical features complicate this multi-faceted problem? And how to evaluate the reliability and usability of geoparsers? The major contributions of this work are an open-source geoparser for Finnish texts, Finger, and two test datasets, or corpora, for testing Finnish geoparsers. One of the datasets consists of tweets and the other of news articles. All of these resources, including the relevant code for acquiring the test data and evaluating the geoparser, are shared openly. Geoparsing can be divided into two sub-tasks: recognizing toponyms amid text flows and resolving them to the correct coordinate location. Both tasks have seen a recent turn to deep learning methods and models, where the input texts are encoded as, for example, word embeddings. Geoparsers are evaluated against gold standard datasets where toponyms and their coordinates are marked. Performance is measured on equivalence and distance-based metrics for toponym recognition and resolution respectively. Finger uses a toponym recognition classifier built on a Finnish BERT model and a simple gazetteer query to resolve the toponyms to coordinate points. The program outputs structured geodata, with input texts and the recognized toponyms and coordinate locations. While the datasets represent different text types in terms of formality and topics, there is little difference in performance when evaluating Finger against them. The overall performance is comparable to the performance of geoparsers of English texts. Error analysis reveals multiple error sources, caused either by the inherent ambiguousness of the studied language and the geographical world or are caused by the processing itself, for example by the lemmatizer. Finger can be improved in multiple ways, such as refining how it analyzes texts and creating more comprehensive evaluation datasets. Similarly, the geoparsing task should move towards more complex linguistic and geographical descriptions than just toponyms and coordinate points. Finger is not, in its current state, a ready source of geodata. However, the system has potential to be the first step for geoparsers for Finnish and it can be a steppingstone for future applied research

Citizen science characterization of meanings of toponyms of Kenya: a shared heritage

This paper examines the toponymic heritage used in Kenya’s Authoritative Geographic Information (AGI) toponyms database of 26,600 gazetteer records through documentation and characterization of meanings of place names in topographic mapping. A comparison was carried out between AGI and GeoNames and between AGI and OpenStreetMap (OSM) volunteered records. A total of 15,000 toponymic matchings were found. Out of these, 1567 toponyms were then extracted for further scrutiny using AGI data in the historical records and from respondents on toponyms’ meanings. Experts in toponymy assisted in verifying these data. From the questionnaire responses, 235 names occurred in more than one place while AGI data had 284. The elements used to characterize the toponyms included historical perceptions of heritage evident in toponyms in their localities, ethnographic, toponymical and morphology studies on Kenya's dialects. There was no significant relationship established between the same place name usages among dialects as indicated by a positive weak correlation r (438), = 0.166, p < 0.001 based on the effect of using the related places and the distance between related places. The weak correlation implies that the one name one place principle does not apply due to diverse language boundaries, strong bonds associated with historical toponyms in the form of heritage and significant variations on how names resist changes to preserve their heritage