Cláudio E. C. Campelo

A context-aware system based on service-oriented architecture

Advances in mobile devices, sensors and wireless networks have motivated the development of context-aware applications. Such mobile applications use sensors for monitoring several features such as: location, temperature, velocity, weather, traffic jam, noise, air pollution, and so on. This monitoring enables service provision according to a given context. These applications are known as context-aware systems. Context-aware applications are sensible to user necessities, personalized according to his profile, requirements and context. They evaluate user environment and push information that is relevant to user context. In this paper we present Omnipresent, which is a service-oriented architecture for context-aware applications. Omnipresent may be accessed from either mobile devices or Web browsers, and it is based on Web services and well-established standards for LBS applications such as those proposed by the OpenGeoSpatial Consortium. Omnipresent offers several services: map presentation, routing, advertisement, and also works as a reminder tool.

Representing and Reasoning about Changing Spatial Extensions of Geographic Features

This paper presents a logical framework for representing and reasoning about events and processes which are characterised by changes in the spatial extension of geographic features. This work addresses some crucial (and still open) issues on the modelling of geographic dynamics, such as how to represent the relationship between events, processes and geographic features and how to relate event and process types to their particular instances; how to handle spatial and temporal vagueness to associate specific spatio-temporal boundaries with events and process instances; and how to provide a flexible representation which can be applied to distinct geographic phenomena as well as which can accommodate different standpoints on these phenomena. Particular focus is placed on phenomena which can be identified from spatio-temporal data of the geographic domain, and therefore a discussion is given on the way the logical framework can be linked to data. Finally, we present a system prototype implemented to experiment the proposed logical framework with real data.

A Model for Geographic Knowledge Extraction on Web Documents

There is an increasing interest on doing research in the field of information retrieval which aims to incorporate new dimensions, apart from text based retrieval, to the Web search engines. Geographical Information Retrieval (GIR) aims to index Web resources using a geographic context. The process of identifying the geographic context starts with the detection of different types of geographic references associated to the documents, as for example, the occurrence of place names. This paper presents a model for detecting geographic references in Web documents based on a set of heuristics. Moreover, new concepts and methods for disambiguation of many places with the same name are addressed. Finally, a prototype was built, called GeoSEn which aimed to validate the effectiveness of the proposed model.

Geographic scope modeling for web documents

Geographic Information Retrieval (GIR) has become a very attractive area of research. GIR is a specialization of a traditional information retrieval system, which may index and search Web documents based on their spatial footprints. Research in this new field may be categorized into crawling spatial-related documents, modeling the geographic scope of a document, indexing these documents using textual and spatial features, and the building of spatially-enabled searching and ranking. This paper presents a method for modeling the geographic scope. The proposed model is based on both the statistics collected from the detected references; and the spatial distribution of the places involved in a given document. This model aims to simplify the indexing and searching processes. Furthermore, the number of spatial operations is reduced, as a consequence the overall performance is improved.

Identifying geographical processes from time-stamped data

Humans tend to interpret a temporal series of geographical spatial data in terms of geographical processes. They also often ascribe certain properties to processes (e.g. a process may be said to accelerate). Given a spatial region of observation, distinct properties may be observed in different subregions and at different times, which causes difficulties for humans to identify them. The conceptualisation of geographical features and their correlation with geographical phenomena may provide a human like approach to analyse large spatio-temporal datasets. This paper presents a representational model and a reasoning mechanism to analyse evolving geographical features and their relationship to geographical processes. The proposed approach comprises methods of relating occurrences of geographical events to geographical processes which is said to proceed over time. We introduce an initial set of properties which can be associated with several geographical processes. We consider this as a first step towards a more general model for representing and reasoning about geographical processes.

From polygons and timestamps to dynamic geographic features: grounding a spatio-temporal geo-ontology

This paper presents a knowledge representation approach to modelling and manipulating spatio-temporal data and to grounding a spatio-temporal geographic ontology upon the data. This approach has been developed in the form of a definition-based ontological framework, upon which GIS applications can be developed to perform analysis of geographic phenomena by querying the spatio-temporal database in a more conceptualised fashion. We draw special attention to the representation of geographic features which can change over time, since an appropriate modelling of these dynamic features can provide a natural way of defining other dynamic entities of geographic space, such as events and processes. In addition, the paper discusses some architectural aspects of a GIS which incorporates our semantic model and describes an example of event modelling to illustrate the application of the proposed approach.

A user-personalized model for real time destination and route prediction

The act of predicting a destination and route a user will take, as soon as he/she begins to move, has several benefits. A system with this kind of information is able to help the user to avoid a congested route or to suggest a Place of Interest (POI). Nowadays, the task of tracking a user movement is more feasible thanks to current smartphones, with embedded GPS devices. Many related work addresses the problem considering only next place prediction. They do not focus on route and destination prediction. Moreover, many models for place prediction only predict a place already visited by the user. This paper proposes a user-personalized model for predicting route and destination, including places where user has never visited before. Our model works automatically, i.e., without user interactions. The experiment was conducted with a real and public dataset, containing data from 21 users collected during three months, and the results were encouraging, with a precision of 76.9%.

A gold-standard social media corpus for urban issues

This paper introduces a gold-standard corpus extracted from manually labeled tweets concerning urban issues. The main contribution is to provide a labeled tweet dataset which can be useful for building machine-learning classifiers in the urban issues domain, including geographical features. Thus, this corpus can also be useful for improving geoparsers to correctly identify place names in urban such as Points-of-Interest (POI), Streets/Roads and Districts. Our method for building the corpus includes human-volunteer quality assessment and human-driven labeling using an ad hoc web application, the Tweet Annotator. The volunteers were asked to complete a feedback survey in order to identify the main difficulties during the labeling task. In this paper, we also report the findings from a case study carried out to analyze the spatial relationships in the generated corpus for the locations which a tweet may refer to: the geocoded, the user home and the mentioned ones.

Gazetteer enrichment for addressing urban areas: a case study

The advent of volunteered geographical information (VGI) has contributed to the growth of the amount of user-contributed spatial data around the world. Spatial data acquired from crowdsourcing environments may contain valuable information which can be useful in other research fields, such as Digital gazetteers, commonly used in Geographic Information Retrieval. Digital gazetteers have a powerful role in the geoparsing process. They need to be kept up-to-date and as comprehensive as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital documents. The detection of toponyms in digital texts such as social media posts is a bottom line for discovering useful spatially related information such as complaints regarding urban areas. In this context, this article proposes a method for gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied within a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter posts focusing solely on the messages aiming at evaluating the performance of the enriched system. The obtained results were encouraging and have provided a good basis for discussion.

Leveraging VGI for Gazetteer Enrichment: A Case Study for Geoparsing Twitter Messages

With the advent of Volunteered Geographical Information (VGI), the amount of user-contributed spatial data grows around the world each day. Such spatial data may contain valuable information which may help other research fields, such as the Digital Gazetteers used in Geographic Information Retrieval (GIR), for instance. The Digital Gazetteers have a powerful role in the geoparsing process. They need to keep themselves up-to-date and as complete as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital texts. In this context, this paper proposes a method of gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied in a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter messages focused solely on the microtexts in order to evaluate the performance of the enriched system. The results obtained were compared with previous results of a case study that used the same dataset and both the gazetteer and the geoparser without improvements.