2,695 research outputs found
FragViz: visualization of fragmented networks
BACKGROUND
Researchers in systems biology use network visualization to summarize the results of their analysis. Such networks often include unconnected components, which popular network alignment algorithms place arbitrarily with respect to the rest of the network. This can lead to misinterpretations due to the proximity of otherwise unrelated elements.
RESULTS
We propose a new network layout optimization technique called FragViz which can incorporate additional information on relations between unconnected network components. It uses a two-step approach by first arranging the nodes within each of the components and then placing the components so that their proximity in the network corresponds to their relatedness. In the experimental study with the leukemia gene networks we demonstrate that FragViz can obtain network layouts which are more interpretable and hold additional information that could not be exposed using classical network layout optimization algorithms.
CONCLUSIONS
Network visualization relies on computational techniques for proper placement of objects under consideration. These algorithms need to be fast so that they can be incorporated in responsive interfaces required by the explorative data analysis environments. Our layout optimization technique FragViz meets these requirements and specifically addresses the visualization of fragmented networks, for which standard algorithms do not consider similarities between unconnected components. The experiments confirmed the claims on speed and accuracy of the proposed solution
Memetic algorithms for ontology alignment
2011 - 2012Semantic interoperability represents the capability of two or more systems to
meaningfully and accurately interpret the exchanged data so as to produce
useful results. It is an essential feature of all distributed and open knowledge
based systems designed for both e-government and private businesses, since it
enables machine interpretation, inferencing and computable logic.
Unfortunately, the task of achieving semantic interoperability is very difficult
because it requires that the meanings of any data must be specified in an
appropriate detail in order to resolve any potential ambiguity.
Currently, the best technology recognized for achieving such level of precision
in specification of meaning is represented by ontologies. According to the
most frequently referenced definition [1], an ontology is an explicit
specification of a conceptualization, i.e., the formal specification of the
objects, concepts, and other entities that are presumed to exist in some area of
interest and the relationships that hold them [2]. However, different tasks or
different points of view lead ontology designers to produce different
conceptualizations of the same domain of interest. This means that the
subjectivity of the ontology modeling results in the creation of heterogeneous
ontologies characterized by terminological and conceptual discrepancies.
Examples of these discrepancies are the use of different words to name the
same concept, the use of the same word to name different concepts, the
creation of hierarchies for a specific domain region with different levels of
detail and so on. The arising so-called semantic heterogeneity problem
represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]XI n.s
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The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
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