3,891 research outputs found
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
Interoperability and machine-to-machine translation model with mappings to machine learning tasks
Modern large-scale automation systems integrate thousands to hundreds of
thousands of physical sensors and actuators. Demands for more flexible
reconfiguration of production systems and optimization across different
information models, standards and legacy systems challenge current system
interoperability concepts. Automatic semantic translation across information
models and standards is an increasingly important problem that needs to be
addressed to fulfill these demands in a cost-efficient manner under constraints
of human capacity and resources in relation to timing requirements and system
complexity. Here we define a translator-based operational interoperability
model for interacting cyber-physical systems in mathematical terms, which
includes system identification and ontology-based translation as special cases.
We present alternative mathematical definitions of the translator learning task
and mappings to similar machine learning tasks and solutions based on recent
developments in machine learning. Possibilities to learn translators between
artefacts without a common physical context, for example in simulations of
digital twins and across layers of the automation pyramid are briefly
discussed.Comment: 7 pages, 2 figures, 1 table, 1 listing. Submitted to the IEEE
International Conference on Industrial Informatics 2019, INDIN'1
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