Geographic Information Retrieval

ÖZET Bu çalışmada; öncelikle coğrafi/konumla ilgili bilgiler ve bu bilgileri ihtiva eden bilgi kaynaklarına erişmek için kullanılan bir kavram olan coğrafi bilgi erişim kavramı ve çalışma prensipleri ile coğrafi bilgi erişim sistemleri incelenmiştir. Açık devlete giden yolda açık coğrafi veri kavramı da açık veri, devlet verisi ve açık devlet verisi bileşenleri içerisinde ele alınmıştır. Açık devlet verisi türlerinden birisi olan açık coğrafi veriye erişim konusu çalışmanın özünü oluşturmaktadır. Erişim ve paylaşımı sağlayan Konumsal Veri Altyapıları (KVA) ve açık coğrafi veri kavramının dünyada algılanış biçimleri de araştırmaya dâhil edilmiştir. Elde edilen bulgular ışığında, Türkiye'de açık devlet anlayışına giden yolda yürütülen açık veri ve açık coğrafi/konumsal veri çalışmaları incelenmiş, ulusal bir coğrafi bilgi erişim sistemi kurulamamasının nedenleri değerlendirilerek ulusal coğrafi bilgi erişim konusu kapsamında öneriler sunulmuştur.
ABSTRACT In this study, previously the geographic information retrieval concept and its principles with geographic information retrieval systems were examined, which used for retrieve geographic/geospatial information and knowledge sources. Accordingly, open geographic data concept analyzed with the components of open data, government data and open government data. Open geographic data retrieval constitutes the core of the study. Also, Spatial Data Infrastructures (SDI) that enables retriving and sharing data and perception manners of open geographic data concept at world integrated the study. With the evaluated results, open data and open geographic/geospatial data exercises of Turkey in the past few years investigated for open government consciousness. Thus, reasons of the not establishing a national geographic information retrieval system evaluated and some recommendations provided on national geographic information retrieval.

Gazetteers are important tools used in a wide variety of workflows that depend on linking natural language text to geographical space. The spatial properties of these data sources, such as coverage, balance, and completeness, affect the performance of common tasks such as geoparsing and geocoding. However, little attention has focused on how these properties vary in global gazetteers, particularly across country boundaries and according to feature types. In this paper, we present a detailed investigation of the spatial properties of two open gazetteers with worldwide coverage: GeoNames, and the Getty Thesaurus of Geographic Names (TGN). Using point density maps, correlations, and linear regressions, we analyze the global spatial coverage of each data source for the full set of features and for top feature types: populated places, streams, mountains, and hills. Results show wide discrepancies in coverage between the two datasets, sharp changes in feature type coverage across country borders, and idiosyncratic patterns dominated by a few countries for the more sparsely covered natural features. As more and more systems rely on recognizing and grounding named places, these patterns can influence the analysis of growing amounts of online text content and reinforce or amplify existing inequalities.

Gazetteers are playing a central role in the current data revolution as key tools to link content to geographical space. However, their geographies and idiosyncrasies are poorly understood despite their potential to worsen application outcomes and information inequalities. In this study, we analyze two open gazetteers, GeoNames and the Getty Thesaurus of Geographic Names, in terms of the quantity and spatial distribution of features in Great Britain, illustrating how they provide a different and inconsistent picture of the region and are still far less detailed than the institutional gazetteers curated by Ordnance Survey.

How do people perceive landscapes? What elements of the landscape do they identify as characteristic of a landscape? And how can we arrive at descriptions, and ultimately representations that better reflect people’s notions of landscapes? In this study, we collected landscape descriptions from five landscape types at 10 study sites in Switzerland. For each site, we collected data from three sources: free lists with participants, hiking blogs, and Flickr tags. Free lists were obtained through on-site interviews with visitors, hiking blogs were gathered by focused crawling of web content, and Flickr tags were selected based on spatial footprints obtained from the hiking blogs. We quantitatively compared landscape descriptions between data sources and landscape types using cosine similarity. We found that descriptions from the same data source were significantly more similar, irrespective of landscape type. Descriptions from the same landscape type were more similar, but only within the same data source. Through a qualitative analysis of different aspects of landscape in our content, we found that each data source offered a different distribution of landscape aspects. For example, while Flickr tags contained high proportions of toponyms, they contained little content relating to sense of place. In contrast, hiking blogs contained more information about sense of place. Our approach combining these varied textual sources thus offers a more holistic view on landscapes. This study constitutes a step toward extracting semantically rich descriptions of landscapes from a variety of sources and using this information to distinguish different landscapes, with potential applications for landscape monitoring and management.

This paper presents an automatic annotation of spatial expressions enabling the improvement of the indexing process of non-structured geographic information. The proposed method combines the use of specific intra-sentential relationships and external resources. It relies on expanded spatial named entities recognition to deduce a symbolic representation ex- pressed in terms of semantic features, on the one hand, and, to give them a numerical representation, on the other hand. Our method completely implemented and was tested on a corpus of travelogues.

Geographic Information Retrieval (GIR) systems rely on the identification and disambiguation of place names in documents to determine the region about which they are relevant. The place names are mapped into geographic concepts and used to assign an encompassing concept (a scope) to each document. However, sometimes a single scope is too restrictive and insufficient for capturing the geographic semantics of a document. We propose as an alternative to abstract the geographic semantics of a document as a geographic signature, which is a list of maximally disambiguated geographic references found in a document. A signature can be used in multiple GIR applications, such as in building a geographic index for a document collection. We perform the disambiguation of the possible geographic meanings using semantic similarity measures.

1University of Hildesheim, Germany, 2University of California, Berkeley, CA, USA 3University of Padua, Italy , 4University of Sheffield, Sheffield, UK, 5Linguateca, SINTEF ICT, Norway E-mail: [email protected] ... In this paper we present an evaluation resource for ...

For the participation of the University of Alicante in the first cross-language Geographic Information Retrieval, we have developed a system made up of three modules. One of them is an Information Retrieval module and the others are Named Entity Recognition modules based on machine learning and based on knowledge. We have carried out several runs with different combinations of these modules for resolving the monolingual and bilingual tasks. The system obtained better result in monolingual task achieving an improvement between 48 % and 69 % above the average. The results are shown and discussed in the paper.

Historical itineraries, often accessible as lists or tables describing places visited in sequence, are abundant resources and also important objects of study for humanities scholars. This article advances a novel method for automatically geocoding tabular itineraries, combining approximate string matching with a cost optimization algorithm based on dynamic programming. Experiments with a dataset of historical itineraries, with ground-truth geocoding annotations provided by domain experts and leveraging also the GeoN-ames gazetteer, attest to the effectiveness of the proposed method. The obtained results show that while approximate string matching can already achieve very low median errors, with many toponyms matching exactly against GeoNames entries, the combination with cost optimization can significantly improve results in terms of the average distance towards the correct disambiguations.

The aim of this article is to present new research showcasing how Geographic Information Systems in combination with Natural Language Processing and Corpus Linguistics methods can offer innovative venues of research to analyse large textual collections in the Humanities, and particularly, in historical research. Using as example the collection of the Registrar General’s Reports containing more than 200,000 pages of descriptions, census data and vital statistics for the UK, we introduce newly developed automated textual tools and spatial analyses used to investigate a case study about the references made to cholera and other diseases in these historical sources, and their relationship to place-names during Victorian times. The integration of such techniques has allowed us to explore in an automatic way this historical source containing millions of words, to examine the geographies depicted in it, and to identify textual and geographic patterns in the corpus.

Geographic information is increasingly used in mobile contexts. Mobility constrains several aspects of this mobile usage, such as limited screen estate, number of desired interactions, or availability due to battery time. As a consequence designers and developers of mobile geographic applications strive to reduce the amount of information delivered to users and aim at serving just useful content rather than a larger amount of unspecific data. This is to address the problem of information overload as well as to provide information that is relevant. Ultimately this is the goal of any information retrieval system.
Thus, we want to set off by studying how relevance of information is defined and handled in information science/information retrieval (IR), Geographic Information Retrieval (GIR), Mobile Information Retrieval (MIR), Location Based Services (LBS), and Geographic Relevance (GR). The objective is to reveal commonalities as well as differences among these fields, mainly between GIR and GR, and eventually show potential mutual benefits.

GeoTime for the NTCIR Workshop 9 is the second evaluation of Geographic and Temporal Information Retrieval called “NTCIR GeoTime”. The focus of this task is on search with Geographic and Temporal constraints. This overview describes the data collections (Japanese and English news stories), topic development, assessment results and lessons learned from this second NTCIR GeoTime task, which combines GIR with timebased search to find specific events in a multilingual collection. Six teams submitted Japanese runs and nine teams submitted English runs. Three teams participated in both Japanese and English.

In this paper we test the hypothesis Given a piece of text describing an object or concept our combined disambiguation method can disambiguate whether it is a place and ground it to a Getty Thesaurus of Geographical Names unique identifier with significantly more accuracy than naive methods. We demonstrate a carefully engineered rule-based place name disambiguation system and give Wikipedia as a worked example with hand-generated ground truth and bench mark tests. This paper outlines our plans to apply the co-occurrence models generated with Wikipedia to solve the problem of disambiguating place names in text using supervised learning techniques.

Libraries and researchers face a big challenge: sometimes thousands, millions of maps are being conserved in archives to retard deterioration, but this makes it hard to obtain specific knowledge of their content, sometimes even of their existence. In recent years, the efforts have increased to digitise historical documents and maps to preserve them digitally, make them accessible and allow researchers and the general public a less restricted access to their heritage. But maps are more than a cultural artifact: they are data. Data about the past that can very well be important for science and decision-making today.
The problem is, the vast amount alone makes it hard to know what to look for. Maps are usually archived and catalogued with limited meta-information, sometimes obscuring their actual content. This makes it impossible to find specific information without expert knowl-edge on history and cartography. Extracting the content, i.e. data, of scanned historical maps is a necessity to make the content searchable and to use modern digital tools for automatic processing and analyses.
To combine data from different maps and compare them with modern geospatial data, georeferencing is paramount. This is usually done by hand and needs a lot of time and spe-cialised training. We explore if and how usable GCP can be found automatically in historical maps with computer vision and document analysis methods of today. In this work-in-progress report, we use OCR on map labels and geocoding of persisting geographical feature designation for successful first experiments. This shows text recognition and vectorisation to be a promising research direc-tion for large-scale automated georeferencing of historical maps.

1University of Hildesheim, Germany, 2University of California, Berkeley, CA, USA 3University of Padua, Italy , 4University of Sheffield, Sheffield, UK, 5Linguateca, SINTEF ICT, Norway E-mail: [email protected] ... In this paper we present an evaluation resource for ...

In just twenty years, the World Wide Web and mobile networks have changed the way we communicate, the way we search for information we need, and the way we organize our activities. The opportunities aff�orded by these changes led to the development of literally millions of applications, including location-based services, which aim to provide information based on a user's position. However, the search methods used by such
mobile information services have been criticized, because of the simplistic approach they employ.

Aim of this dissertation is to o�er an in-depth study of the concept of relevance in mobile information services. The term "geographic relevance" has been proposed in literature to address such a concept, in order to emphasize the importance of space and time in assessing relevance in mobile information services. In this dissertation, I provide a detailed discussion of the concept of geographic relevance, o�er a conceptual
model of geographic relevance, and propose a list of novel criteria of relevance to be used in assessing the geographic relevance of geographic objects in mobile information services. Two empirical studies are presented, which con�rm the validity of the proposed model and criteria. Based on these outcomes, I propose a computational method for the assessment of geographic relevance. The results show that the proposed assessment method is able to replicate human judgements of geographic relevance. Therefore, I argue
that the proposed approach is an eff�ective method for assessing geographic relevance in mobile information services.
The outcomes of the empirical studies presented in this dissertation con�rm previous studies, showing how spatial proximity is not suff�cient to consider a geographic object as relevant -- assuming it is what a user is searching for. The temporal aspect plays a fundamental role, as temporally unavailable objects are to be considered as non-relevant. Moreover, in this dissertation, I suggest and prove that the geographic environment of a place or object has to be taken into account when assessing its geographic relevance. The
presence of more objects of the same category in a neighborhood, or the presence of objects belonging to correlated categories, increases the relevance of the object. Geographic relevance is also dependent on the opportunities off�ered to a user in the neighborhood. Finally, as a user commonly seeks for places in order to perform an activity, I advocate the importance of taking into account the consequences of a user performing an activity in a given place, with respect to previous, concurrent, and subsequent activities, when assessing the geographic relevance of places or geographic objects.

Gazetteers are dictionaries of geographic place-names that have important implications far beyond the worlds of geographers and cartographers. By containing ‘definitive’ lists of places, gazetteers have the ontological power to define what will and will not be geocoded and represented in databases, maps, search engines, and ultimately our spatial understandings of place. This paper focuses attention on GeoNames, which is the world's largest freely available and widely used gazetteer. We illustrate how content in GeoNames is characterised by highly uneven spatial distributions. There are dense clusters of place-names in some parts of the world and a relative absence of geographic content in others. These patterns are related not just to the wealth and population size of a country, but also to its policies on Internet access and open data. The paper then traces some of the specific implications of this information inequality: showing how biases in gazetteers are propagated in a variety of geographic meaning-making.

In this paper we demonstrate how a large corpus, consisting of about 10000 articles describing Swiss alpine landscapes and activities and dating back to 1864, can be used to explore the use of language in space. In a first step we link landscape descriptions to geospatial footprints, which requires new methods to disambiguating toponyms referring to natural features. Secondly, we identify natural features used to describe landscapes, which are compared and discussed in the light of previous work based on controlled participant experiments in laboratory settings and more exploratory ethnographic studies. Finally, we use natural features in combination with geospatial footprints to investigate variations in landscape descriptions across space.
Our contributions are threefold. Firstly, we show how a corpus composed of detailed descriptions of natural landscapes can be georeferenced and mapped using density surfaces and an adaptive grid linking footprints to articles.  Secondly, 95 natural features are identified in the corpus, forming a vocabulary of terms reflecting known basic levels and their relationships to other more specific landscape features. Thirdly, we can explore the use of natural features in broader spatial and temporal context than is possible in typical ethnographic work, by exploring when and where particular terms are used within Switzerland with respect to our corpus. On the one hand this enables us to characterise individual regions, and on the other, to measure similarity between regions, on the basis of associated natural features.
Our methods could be adapted to different types of corpus, for instance referring to fine granularity entities in urban landscapes. Our results are potential building blocks for attaching place-related descriptions to automatically generated sensor data such as photographs or satellite images.

This short paper investigates how locations in or close to
water masses in topics and documents (e.g. rivers, seas,
oceans) are referred to. For this study, 13 topics from the
GeoCLEF topics 2005-2008 aiming at documents on rivers,
oceans, or sea names were selected and the corresponding
relevant documents retrieved and manually annotated.
Results of the geographic annotation indicate that i) topics
aiming at locations close to water contain a wide variety
of spatial relations (indicated by dierent prepositions), ii)
unnamed locations can be generated on-the-fly by referring
to movable objects (e.g. ships, planes) travelling along a
path, iii) underspecied regions are referenced by proximity
or distance or directional relations. In addition, several
generic expressions (e.g. "in international waters") are frequently
used, but refer to different underspecied regions.

This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst’s or researcher’s ability

In this paper we describe the geographic information retrieval system developed by the Multimedia & Information Systems team for GeoCLEF 2006 and the results achieved. We detail our methods for generating and applying co-occurrence models for the purpose of place name disambiguation, our use of named entity recognition tools and text indexing applications. The presented system is split into two stages: a batch text & geographic indexer and a real time query engine. The query engine takes manually crafted queries where the text component is separated from the geographic component. Two monolingual runs were submitted for the GeoCLEF evaluation, the first constructed from the title and description, the second included the narrative also. We explain in detail our use of co-occurrence models for place name disambiguation using a model generated from Wikipedia. The paper concludes with a full description of future work and ways in which the system could be optimised.

With the rapid use of the internet into users' daily lives, it is imperative to capture location-specific information from local database to satisfy the user's local information needs [context]. This is especially true in the increasingly local search environments. There are a number of local Web search engines enabling users to find information which is location specific Web content. In this aspect web search engine gives sets of relevant documents related to user query. Thereby user has to scan all searched documents and read line by ...