Geographic Information Retrieval System Research Articles

Flexible Trip-Planning Queries

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes’ identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood.

Multimedia and geographic data integration for cultural heritage information retrieval

In this paper a system providing an efficient integration between Content-Based Image Retrieval (CBIR) and Geographic Information Retrieval (GIR) is presented. Over the years, many CBIR systems have been proposed to give a solution for an efficient use of multimedia/visual contents and other issues as performance, quality of retrieval, data heterogeneity, and multimodal information integration. The aim of the proposed approach is to prove that the use of geographic data can improve the results obtained by an image matching system based only on visual data. Our framework is composed of three parts, each of them described in detail in this paper: the first part is dedicated to CBIR, with an experimental comparison of a large number of different multimedia features to choose the one to use in the system implementation; in the second part the methodology to integrate geographic and multimedia data is showed; in the last part is presented a GIR system implementation using a “points of interest” search. An Android application has been developed for the client-side using Apache Solr as server side provider for the information retrieval functionalities. An experimental evaluation is carried out to demonstrate the effective improvement given by the combination of geographic and multimedia data. Our results have been obtained using a real dataset composed of artworks located in Naples’s museums.

Geotagging Text Data on the Web—A Geometrical Approach

Geotagging, the process of tagging of documents with geographical information, is an important issue in geographical information retrieval systems. In this paper, we propose a novel approach for geotagging of textual web documents. Existing techniques disambiguate place names occurring in a document individually first and then find its focus. As opposed to this, the proposed approach, considers all the place names together without disambiguating them individually. It is a heuristic-based three step process. The first step is hierarchical, the second one is geometric technique, and last step reports the final focus. The approach requires only a gazetteer and a named entity recognition tool for processing. It reports the geographical focus of a web page as a tuple (latitude and longitude). We have tested our algorithm on two data sets, namely, the Wikipedia and Open Directory Project (ODP) data sets. The proposed approach reports the focus correctly up to continent level for 97.07% of the Wikipedia pages and correctly up to country level for 95.57% of them. Moreover, for more than 60% of the documents, the error in the distance between the actual focus, and the reported focus is within 15 km, i.e., less than 10 miles for this data set. In addition, the median error is 8.17 km. The technique also performs very well on huge data sets like ODP with sample size of around 600000 web pages. The proposed system can be used in any geographic information retrieval system where geographic meta-data of documents needs to be obtained for indexing and searching.

Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text

Significant amounts of information available today contain references to places on earth. Traditionally such information has been held as structured data and was the concern of Geographic Information Systems (GIS). However, increasing amounts of data in the form of unstructured text are available for indexing and retrieval that also contain spatial references. This monograph describes the field of Geographic Information Retrieval (GIR) that seeks to develop spatially-aware search systems and support user’s geographical information needs. Important concepts with respect to storing, querying and analysing geographical information in computers are introduced, before user needs and interaction in the context of GIR are explored. The task of associating documents with coordinates, prior to their indexing and ranking forms the core of any GIR system, and different approaches and their implications are discussed. Evaluating the resulting systems and their components, and different paradigms for doing so continue to be an important area of research in GIR and are illustrated through several examples. The monograph provides an overview of the research field, and in so doing identifies key remaining research challenges in GIR.

Keyword and Event Extraction for Thematic Map Retrieval from Indonesian Online News Site

Online news sites provide great deal of information that may be extracted and mined to create Geographical Information Retrieval (GIR) system that provides thematic map based on user query. The task requires extracting generic events and its attributes from the news corpus. This event extraction requires the keywords and topics that can help increase the accuracy of the extraction and retrieval process. We prepare a large online news corpus and compared the Latent Dirichlet Allocation (LDA) and CBOW and skip-gram model to help providing base thematic keywords which can assist the extraction process on the intended GIR system. LDA is better in terms of semantic relatedness and the CBOW and skip-gram is useful for providing semantic similarity.

Table Extraction from Web Pages Using Conditional Random Fields to Extract Toponym Related Data

Table is one of the ways to visualize information on web pages. The abundant number of web pages that compose the World Wide Web has been the motivation of information extraction and information retrieval research, including the research for table extraction. Besides, there is a need for a system which is designed to specifically handle location-related information. Based on this background, this research is conducted to provide a way to extract location-related data from web tables so that it can be used in the development of Geographic Information Retrieval (GIR) system. The location-related data will be identified by the toponym (location name). In this research, a rule-based approach with gazetteer is used to recognize toponym from web table. Meanwhile, to extract data from a table, a combination of rule-based approach and statistical-based approach is used. On the statistical-based approach, Conditional Random Fields (CRF) model is used to understand the schema of the table. The result of table extraction is presented on JSON format. If a web table contains toponym, a field will be added on the JSON document to store the toponym values. This field can be used to index the table data in accordance to the toponym, which then can be used in the development of GIR system.

Development and evaluation of a geographic information retrieval system using fine grained toponyms

Geographic information retrieval (GIR) is concerned with returning information in response to an information need, typically expressed in terms of a thematic and spa- tial component linked by a spatial relationship. However, evaluation initiatives have of- ten failed to show significant differences between simple text baselines and more complex spatially enabled GIR approaches. We explore the effectiveness of three systems (a text baseline, spatial query expansion, and a full GIR system utilizing both text and spatial in- dexes) at retrieving documents from a corpus describing mountaineering expeditions, cen- tred around fine grained toponyms. To allow evaluation, we use user generated content (UGC) in the form of metadata associated with individual articles to build a test collec- tion of queries and judgments. The test collection allowed us to demonstrate that a GIR- based method significantly outperformed a text baseline for all but very specific queries associated with very small query radii. We argue that such approaches to test collection development have much to offer in the evaluation of GIR.

Thematic signatures for cleansing and enriching place-related linked data

There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data.

Ranking approaches for GIR

At the core of any information retrieval system is its method for ranking results in response to a user's query. Geographic Information Retrieval (GIR) systems have an added complexity for this task since the information to be included in the ranking process goes beyond text and word frequency information to encompass geographic proximity, containment and other spatial operations. The need to combine both geographic and text components into GIR systems has led to some interesting hybrid approaches in addition to the "pure" spatial ranking methods based on spatial similarity. In this short survey I will look at some of the methods that have been reported in the literature and used in GIR evaluations including GeoCLEF and NTCIR GeoTime.

Challenges for indexing in GIR

Geographic information retrieval (GIR) has been evaluated in campaigns such as GeoCLEF, GikiP, and GikiCLEF [8, 12, 11]. Surprisingly, most results from these evaluations showed that adding more geographic knowledge typically had little or no effect on performance of GIR systems or that it even decreases performance compared to traditional (textual) information retrieval baselines (see e.g. [4]). In this position paper, current challenges of how to further improve the creation, structure and access to geographic resources (for simplicity, called the geographic index in the rest of this note) are discussed. The major challenges for indexing in GIR discussed in this note are applying methods beyond named entity recognition to identify geographic references, integrating additional, proven methods from related research areas such as question answering for semantic indexing, and aiming for better index support to interpret geographic relations. After summarizing the state of the art in indexing for GIR as it has evolved from GIR evaluation campaigns, research challenges and directions for future research are presented.

Geographic information retrieval: Modeling uncertainty of user's context

Geographic information retrieval (GIR) is nowadays a hot research issue that involves the management of uncertainty and imprecision and the modeling of user preferences and context. Indexing the geographic content of documents implies dealing with the ambiguity, synonymy and homonymy of geographic names in texts. On the other side, the evaluation of queries specifying both content based conditions and spatial conditions on documents’ contents requires representing the vagueness and context dependency of spatial conditions and the personal user's preferences. The spatial condition can be specified linguistically in the query through vague terms such as “close to the North East of Milan’’, whose semantic depends on the user's context and perception of distance. Further, users may want to express queries in which the content condition and the spatial condition have a distinct preference and are combined with a distinct semantics. In this paper, we propose a geographic information retrieval model and a system implementing it that represents both the uncertainty in indexing the geographic documents’ content and the user's context and preferences in evaluating flexible spatial queries. It extracts the geographic content from documents’ text by applying heuristic knowledge coded by bipolar rules which evaluate positive hints and negative hints for the recognition of geographic names in text. Thus, it represents the geographic content of documents by fuzzy footprints, i.e., distinct locations on the earth associated with the text with a distinct degree of significance. Finally, the system allows evaluating two types of queries flexibly combining the content based condition with the spatial condition. The spatial condition is interpreted as the soft constraint “close’’ on the user's perceived distance between the documents’ footprint and query's footprint. For each retrieved document, two relevance scores are computed with respect to the two query conditions that are flexibly combined to generate an overall ranked list of documents. The user can choose the semantic for the combination that can be either an asymmetric “and possibly’’ aggregation between the mandatory content condition and the optional spatial condition, or a compensative “average’’ aggregation, defined as a linear combination of the two conditions; further, a relative preference between the conditions can be specified to achieve personalization and effectiveness. A prototypal geographic information retrieval system, named Geo-Finder, based on this model is described, and its evaluations are discussed.

Web-based geographic search engine for location-aware search in Singapore

The proliferation of advanced, online, three-dimensional mapping programs provides Web users with detailed aerial and satellite maps, high-powered graphics and animation, and customization capabilities. This mapping revolution can transform the landscape of the Web into a geographic Web, where information can be organized geographically, so that users can understand information in the proper context. In this paper, we describe a geographic information retrieval system that is able to search for location-specific information in Singapore-based Web sites. The user is able to view their search locations on a satellite map instead of the two-dimensional maps currently used in street directories. The Web-based search engine is able to search for locations based on area names, building names, and groups of landmark types, business names, and business categories. Furthermore, the user is also able to use their current coordinates as a parameter so that the search engine is able to return results in order of the distance from the user’s current location.

On the evaluation of Geographic Information Retrieval systems

Search engines for Digital Libraries allow users to retrieve documents according to their contents. They process documents without differentiating the manifold aspects of information. Spatial and temporal dimensions are particularly dismissed. These dimensions are, however, of great interest for users of search engines targeting either the Web or specialized Digital Libraries. Recent studies reported that nearly 20% queries convey spatial and temporal information in addition to topical information. These three dimensions were referred to as parts of “geographic information.” In the literature, search engines handling those dimensions are called “Geographic Information Retrieval (GIR) systems.” Although several initiatives for evaluating GIR systems were undertaken, none was concerned with evaluating these three dimensions altogether. In this article, we address this issue by designing an evaluation framework, usefulness of which is highlighted through a case study involving a test collection and a GIR system. This framework allowed the comparison of our GIR system to state-of-the-art topical approaches. We also performed experiments for measuring performance improvement stemming from each dimension or their combination. We show that combining the three dimensions yields improvement in effectiveness (+73.9%) over a common topical baseline. Moreover, rather than conveying redundancy, the three dimensions complement each other.

Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index

Both Geographic Information Systems and Information Retrieval have been very active research fields in the last decades. Lately, a new research field called Geographic Information Retrieval has appeared from the intersection of these two fields. The main goal of this field is to define index structures and techniques to efficiently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper two contributions to this research field. First, we propose a new index structure that combines an inverted index and a spatial index based on an ontology of geographic space. This structure improves the query capabilities of other proposals. Then, we describe the architecture of a system for geographic information retrieval that defines a workflow for the extraction of the geographic references in documents. The architecture also uses the index structure that we propose to solve pure spatial and textual queries as well as hybrid queries that combine both a textual and a spatial component. Furthermore, query expansion can be performed on geographic references because the index structure is based in an ontology.

Information retrieval with geographical references. Relevant documents filtering vs. query expansion

This is a thorough analysis of two techniques applied to Geographic Information Retrieval (GIR). Previous studies have researched the application of query expansion to improve the selection process of information retrieval systems. This paper emphasizes the effectiveness of the filtering of relevant documents applied to a GIR system, instead of query expansion. Based on the CLEF (Cross Language Evaluation Forum) framework available, several experiments have been run. Some based on query expansion, some on the filtering of relevant documents. The results show that filtering works better in a GIR environment, because relevant documents are not reordered in the final list.

Geographic Information Retrieval for Just Your Surroundings

Geographic information retrieval (GIR) is a new research area that aims at the retrieval of geographic-related documents based not only on keyword relevance but also on geographic relationships between the query and the geographic information in texts. It is natural for people to want information related to just their surroundings. Conventional GIR systems, however, have relatively poor granularity, such as city or province, because they use geographic information in restricted ways -- mostly just for filtering. To address this problem, we propose a geographic scoring method that considers extent implied by each geographic names appeared in texts to emphasize geographic names that focus specific areas, rather than broad geographic names. Furthermore, to improve robustness against errors in pre-processing such as geo-parsing and geo-coding, we also propose a noise elimination method based on clustering. Evaluation is conducted using standard TREC-style evaluation metrics including MAP, R-precision, and so on. The results show that our method outperforms two baseline approaches: full-text search and using the nearest point in the text.

Geographic Information Retrieval and the World Wide Web: A Match Made in Electronic Space

This article looks at the access to geographic information through a review of information science theory and its application to the WWW. The two most common retrieval systems are information and data retrieval. A retrieval system has seven elements: retrieval models, indexing, match and retrieval, relevance, order, query languages and query specification. The goal of information retrieval is to match the user's needs to the information that is in the system. Retrieval of geographic information is a combination of both information and data retrieval. Aids to effective retrieval of geographic information are: query languages that employ icons and natural language, automatic indexing of geographic information, and standardization of geographic information. One area that has seen an explosion of geographic information retrieval systems (GIR's) is the World Wide Web (WWW). The final section of this article discusses how seven WWW GIR's solve the the problem of matching the user's information needs to the information in the system.

An architecture and design approach for a geographical information retrieval system to support retrieval by content and browsing★

In present Geographical Information Systems (GIS), access to information is restricted to experienced users. On the contrary, it is important to permit to final users, easy access to information by content and in a natural way. In fact, a final user is not always an expert on GIS, but he is an expert in a specific application area, such as urban management or land administration. Geographical systems must be able to manage geographical and structured data, together with textual data, and data in other media. In fact, an active use of textual data can add an effective access point for the collection of geographical information. So, it is necessary to include textual-management operations in currently existing geographical systems, in order to provide a useful and comprehensive access point for geographical information collection to nonexpert users of the application. This paper introduces an architecture and design approach for a Geographical Information Retrieval System (GIRS) capable of supporting retrieval by content and browsing on textual data; the architecture and design approach provides the framework for the management of various kinds of media that are necessary to manage in geographical systems.