7,463 research outputs found
Recommended from our members
The role of HG in the analysis of temporal iteration and interaural correlation
Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
Industry~4.0 (I4.0) standards and standardization frameworks have been
proposed with the goal of \emph{empowering interoperability} in smart
factories. These standards enable the description and interaction of the main
components, systems, and processes inside of a smart factory. Due to the
growing number of frameworks and standards, there is an increasing need for
approaches that automatically analyze the landscape of I4.0 standards.
Standardization frameworks classify standards according to their functions into
layers and dimensions. However, similar standards can be classified differently
across the frameworks, producing, thus, interoperability conflicts among them.
Semantic-based approaches that rely on ontologies and knowledge graphs, have
been proposed to represent standards, known relations among them, as well as
their classification according to existing frameworks. Albeit informative, the
structured modeling of the I4.0 landscape only provides the foundations for
detecting interoperability issues. Thus, graph-based analytical methods able to
exploit knowledge encoded by these approaches, are required to uncover
alignments among standards. We study the relatedness among standards and
frameworks based on community analysis to discover knowledge that helps to cope
with interoperability conflicts between standards. We use knowledge graph
embeddings to automatically create these communities exploiting the meaning of
the existing relationships. In particular, we focus on the identification of
similar standards, i.e., communities of standards, and analyze their properties
to detect unknown relations. We empirically evaluate our approach on a
knowledge graph of I4.0 standards using the Trans family of embedding
models for knowledge graph entities. Our results are promising and suggest that
relations among standards can be detected accurately.Comment: 15 pages, 7 figures, DEXA2020 Conferenc
Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?
In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set
- …