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This paper is concerned with interpretation of natural language place descriptions, as they are a rich source of geographic information. A place description is interpreted by matching geographic entities occurring in the text against the OpenStreetMap (OSM) database. This paper is mainly concerned with interpretation of paraphrased places, i.e., entities for which no name is given and which may only by described. Our objective is to determine suitable entity types that allow querying the OpenStreetMap database for the respective place. For example, if we wish to identify a place to eat, we have to check for entities of an a-priori unknown type (cafe, restaurant, etc.). Challenges arise from the open-endedness of language, its ambiguity, and context-sensitivity as well as from mismatches between human conceptualization of place and database ontologies. The contributions of this paper are, first, to present a hard problem that is key to geo-information retrieval beyond named entities. Second, we propose context-sensitive methods for identifying place types based on semantic word similarity. We evaluate the methods on text extracted from Wikipedia and travel blogs, revealing their contribution to advancing automated interpretation of place descriptions to paraphrased places. [ABSTRACT FROM AUTHOR]

The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project "La Città Educante" (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education. [ABSTRACT FROM AUTHOR]

Many natural language tasks related to geographic information retrieval (GIR) require toponym recognition, and identifying Chinese toponyms from social media messages to share real-time information is a critical problem for many practical applications, such as natural disaster response and geolocating. In this article, we focused on toponym recognition from social media messages in Chinese. While existing off-the-shelf Chinese named entity recognition (NER) tools could be applied to identify toponyms, these approaches cannot address a variety of language irregularities taken from social media messages, including location name abbreviations, informal sentence structures and combination toponyms. We present a deep neural network named BERT-BiLSTM-CRF, which extends a basic bidirectional recurrent neural network model (BiLSTM) with the pretraining bidirectional encoder representation from transformers (BERT) representation to handle the toponym recognition task in Chinese text. Using three datasets taken from lists of alternative location names, the experimental results showed that the proposed model can significantly outperform previous Chinese NER models/algorithms and a set of state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, a novel observation-to-generalization place model is proposed. It is shown how this model can be used to formally define the problem of finding geographically similar places. The observation-to-generalization model differentiates between observations of phenomena in the environment at a specific location and time, and generalizations about places that are inferred from these observations. A suite of operations is defined to find similar places based on the invariance of generalized place properties, and it is demonstrated how these functions can be applied to the problem of finding similar places based on the topics that people write about in place descriptions. One use for similar-place search is for exploratory research that will enable investigators to perform case-control studies on place data. [ABSTRACT FROM AUTHOR]

This article reports on the efforts to establish a research project on a geospatial search engine for the Latin American country of Honduras during the author's research stay at a local university. Honduras is an interesting example of the challenges for information and knowledge management in developing countries as it combines many of the issues that are otherwise encountered more isolated or with less impact. These include low Web coverage in a low-resource country with limited Web infrastructure, and generally, work in challenging circumstances. The specific focus on geospatial information uncovers further issues that need to be addressed, such as informal addressing schemes or landmark-oriented location references, broad or incorrect locations for places, or insufficient ground truth in databases. While the tangible results of the project stay behind the original goals, several interesting results were achieved, which are condensed here as an experience report. [ABSTRACT FROM AUTHOR]

The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms. [ABSTRACT FROM AUTHOR]

When users need to find something on the Web that is related to a place, chances are place names will be submitted along with some other keywords to a search engine. However, automatic recognition of geographic characteristics embedded in Web documents, which would allow for a better connection between documents and places, remains a difficult task. We propose an ontology-driven approach to facilitate the process of recognizing, extracting, and geocoding partial or complete references to places embedded in text. Our approach combines an extraction ontology with urban gazetteers and geocoding techniques. This ontology, called OnLocus, is used to guide the discovery of geospatial evidence from the contents of Web pages. We show that addresses and positioning expressions, along with fragments such as postal codes or telephone area codes, provide satisfactory support for local search applications, since they are able to determine approximations to the physical location of services and activities named within Web pages. Our experiments show the feasibility of performing automated address extraction and geocoding to identify locations associated to Web pages. Combining location identifiers with basic addresses improved the precision of extractions and reduced the number of false positive results. [ABSTRACT FROM AUTHOR]

The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As background knowledge of geographic information retrieval (GIR), the gazetteers have their limitations. In this paper we propose to develop and implement a common sense geographic knowledge base (CSGKB) instead of the gazetteers. We define that CSGKB is concerned with the representation of geographic knowledge in human brain and the simulation of geographic reasoning in daily life. Traditional geographic information system (GIS) is based on the model of map with its data based on geographic coordinates and its computation based on geometry. However, CSGKB, which is made up of geographic features and relationships and is based on qualitative spatio-temporal reasoning, can be viewed as the direct model of geographic world. This paper also discusses the characters of CSGKB and presents its structure which is composed of knowledge base, inference engine, geographic ontology and learner. The applications using CSGKB include geographic information retrieval (GIR), natural language processing (NLP), named entity recognition (NER), Semantic Web, etc. At present, our work focuses on the design of geographic ontology and the implementation of the CSGKB knowledge base. In this paper we describe the CSGKB ontology structure, top ontology, geographic location ontology, spatial relationship ontology, and domain ontologies. Finally, we introduce the current state of implementation of CSGKB and give an outlook on our future researches. [ABSTRACT FROM AUTHOR]

Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introducemulti-dimensional scattered ranking, where textually and spatially similar documents are ranked spread in the list, instead of consecutively. The effect of this is that documents close together in the ranked list have less redundant information. We present various ranking methods of this type, efficient algorithms to implement them, and experiments to show the outcome of the methods. [ABSTRACT FROM AUTHOR]