Matching Alternative Addresses: A Semantic Web Approach

2007

In order to create the contents of an address gazetteer service that forms part of a city council Spatial Data Infrastructure, all the existent repositories containing address information in the different council offices must be analyzed and harmonized. The problem is that usually these repositories are constrained by the use of different taxonomies for the identification of urban network feature types. The objective of this work will be to describe how to establish a formal ontology enabling the interoperability among the different taxonomies, and facilitating the construction of the gazetteer contents.

2011

European Handbook of Crowdsourced Information, 2016

Vast swaths of geographic information are produced by non-professional contributors using online collaborative tools. To extract value from the data, creators and consumers alike need some degree of consensus about what the entities of their domain of interest are and how they are related. Traditional information communities, such as government agencies, universities, and corporations, have devised informal and formal mechanisms to reduce the misinterpretation of the data they rely on, curating vocabularies, standards, and, more recently, formal ontologies. Because of the decentralized, fragmented nature of peer production, semantic agreements are more difficult to establish and to document in volunteered geographic information (VGI), severely limiting the re-usability and, ultimately, the value of the data. This paper provides an overview of the semantic issues experienced in VGI, and what potential solutions are emerging from research in geo-semantics and in the Semantic Web. The paradigm of Linked Data is discussed as a promising route to handle the semantic fragmentation of VGI, reducing the friction between data producers and consumers.

HAL (Le Centre pour la Communication Scientifique Directe), 2015

The tremendous increase of data sources containing spatial information is bound up with the diversity of geospatial applications such as location-based services (LBS) and global positioning systems. LBS providers use maps to locate spatial entities referring to points of interest (POI), for instance restaurants or locations of events. In our study, we specifically focus on places that tourists can get through LBS. The multiplication of these providers has an impact on the quality of data: spatial entities referring to the same POI may include spatial and terminological properties with incomplete, inconsistent, inaccurate or even wrong data. Thus, entity matching approaches have been proposed to discover correspondences between spatial entities, and experimental validations are traditionally performed to demonstrate the performance of these approaches in terms of effectiveness and efficiency. However, the datasets used in these experiments are rarely available, thus limiting their reuse and a fair comparison between the proposed approaches. This paper introduces PABench, a benchmark for spatial entity matching, which is available for researchers to assess their algorithms. Our benchmark includes a taxonomy of observed differences, inconsistencies and errors, which enables the characterization of different LBS providers. PABench can provide a complete and accurate evaluation of the different aspects of spatial entity matching approaches, and also facilitate an understanding of their weaknesses and abilities with respect to spatial integration. This paper provides a conceptual platform to enable LBS interoperability.

New Generation Computing, 2010

The combination of the use of advanced Information and Communication Technology, especially the Internet, to enable new ways of working, with the enhanced provision of information and interactive services accessible over different channels, is the foundation of a new family of information systems. Particularly, this information explosion on the Web, which threatens our ability to manage information, has affected the

Transactions in Gis, 2016

A method for matching crowd-sourced and authoritative geospatial data is presented. A level of tolerance is defined as an input parameter as some difference in the geometry representation of a spatial object is to be expected. The method generates matches between spatial objects using location information and lexical information, such as names and types, and verifies consistency of matches using reasoning in qualitative spatial logic and description logic. We test the method by matching geospatial data from OpenStreetMap and the national mapping agencies of Great Britain and France. We also analyze how the level of tolerance affects the precision and recall of matching results for the same geographic area using 12 different levels of tolerance within a range of 1 to 80 meters. The generated matches show potential in helping enrich and update geospatial data.

This paper provides a bibliometric review between integration of authoritative and volunteered geographic information for the purpose of cartographic updating of urban mappings. The adopted methodology was through a bibliometric survey of the literature published by Web of Science and Science Direct. The period was evaluated from 2005 to 2020 and the keywords used were: integration of authoritative data, volunteered geographic information, VGI, large scale topographic mapping, Authoritative urban mapping. The number of publications found was small for the topic that deals with this integration, totalizing 14 articles at Web of Science and 23 at Science Direct. 38% of them were published in the International Journal of Geo Information (ISPRS), 16% in the International Journal of Geographical Information Science. 5% were published in the Cartography and Geographic Information Science and the Computer Geosciences respectively. The other 36% is shown in several other journals, approximately 3% each. Regarding the origin of publications, 25% are in Germany (University of Heidelberg), 14% in the UK (New Castle University), 13% in China (Wuhan University), 11% in Canada (Calgary University), and other countries show percentages between 3% and 5%. Among the research, areas are physical geography, remote sensing, computer science, information science, engineering, and public administration. Among themes addressed in the articles, potentials can be pointed out as existence of models which institutions can implement management of information received collaboratively, existence of several methodologies for quality control of this information so that they can be integrated into authoritative data that are called as data conflation. Methodologies for handling big data and semantic interoperability, as well as automation of processes. This data potential is not only on platforms such as OpenStreetMap, but also on data collected through scraping from social networks such as twitter, sites, and others. Among the challenges, there are still somethings to investigate regarding consideration of temporal, historic, political, and economic aspects, as well as the consideration of legal aspects. The integration of this volunteered geographic information is necessary, mainly in cities with economic and cultural difficulties to maintain their mapping up to date, as well as the difficulty of accessing information that allows access to authoritative data

Concurrency and Computation: Practice and Experience, 2018

Geo-related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real-time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo-related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi-directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.