14,156 research outputs found
Discovering Relations among Named Entities by Detecting Community Structure
PACLIC 20 / Wuhan, China / 1-3 November, 200
DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
Background and Objective: Heterogeneous complex networks are large graphs
consisting of different types of nodes and edges. The knowledge extraction from
these networks is complicated. Moreover, the scale of these networks is
steadily increasing. Thus, scalable methods are required. Methods: In this
paper, two distributed label propagation algorithms for heterogeneous networks,
namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type
of the heterogeneous complex networks. As a case study, we have measured the
efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network
consisting of drugs, diseases, and targets. The subject we have studied in this
network is drug repositioning but our algorithms can be used as general methods
for heterogeneous networks other than the biological network. Results: We
compared the proposed algorithms with similar non-distributed versions of them
namely MINProp and Heter-LP. The experiments revealed the good performance of
the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo
Discovering Functional Communities in Dynamical Networks
Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science"
style. Forthcoming in the proceedings of the workshop "Statistical Network
Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small
clarifications, typo corrections, added referenc
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
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