Context-Aware Presentation of Linked Data on Mobile

2018 ◽  
pp. 1940-1971
Author(s):  
Luca Costabello ◽  
Fabien Gandon
Keyword(s):  
Linked Data ◽  
Edit Distance ◽  
Research Question ◽  
Context Aware ◽  
Graph Edit Distance ◽  
Optimal Error ◽  
Presentation Level ◽  
Rdf Graphs ◽  
Mobile Context ◽  
Proper Context

In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

Context-Aware Presentation of Linked Data on Mobile

2014 ◽  
Vol 10 (4) ◽  
pp. 45-76 ◽  
Author(s):  
Luca Costabello ◽  
Fabien Gandon
Keyword(s):  
Linked Data ◽  
Edit Distance ◽  
Research Question ◽  
Context Aware ◽  
Graph Edit Distance ◽  
Optimal Error ◽  
Presentation Level ◽  
Rdf Graphs ◽  
Mobile Context ◽  
Proper Context

In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

scholarly journals An Interoperable BIM-Based Toolkit for Efficient Renovation in Buildings

Buildings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 271
Author(s):  
Bruno Daniotti ◽  
Cecilia Maria Bolognesi ◽  
Sonia Lupica Spagnolo ◽  
Alberto Pavan ◽  
Martina Signorini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Management System ◽  
Linked Data ◽  
Research Question ◽  
Digital Tools ◽  
European Research ◽  
Construction Sector ◽  
Research Project ◽  
One Step ◽  
Data Environment

Since the buildings and construction sector is one of the main areas responsible for energy consumption and emissions, focusing on their refurbishment and promoting actions in this direction will be helpful to achieve an EU Agenda objective of making Europe climate-neutral by 2050. One step towards the renovation action is the exploitation of digital tools into a BIM framework. The scope of the research contained in this paper is to improve the management of information throughout the different stages of the renovation process, allowing an interoperable exchange of data among the involved stakeholders; the development of an innovative BIM-based toolkit is the answer to the research question. The research and results obtained related with the development of an interoperable BIM-based toolkit for efficient renovation in buildings in the framework of the European research project BIM4EEB. Specifically, the developed BIM management system allows the exchange of the data among the different tools, using open interoperable formats (as IFC) and linked data, in a Common Data Environment, to be used by the different stakeholders. Additionally, the developed tools allow the stakeholders to manage different stages of the renovation process, facilitating efficiencies in terms of time reduction and improving the resulting quality. The validity of each tool with respect to existing practices is demonstrated here, and the strengths and weaknesses of the proposed tools are described in the workflow detailing issues such as interoperability, collaboration, integration of different solutions, and time consuming existing survey processes.

On-line learning the graph edit distance costs

2021 ◽  
Author(s):  
Elena Rica ◽  
Susana Álvarez ◽  
Francesc Serratosa
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
On Line ◽  
On Line Learning

scholarly journals An efficient algorithm for graph edit distance computation

2019 ◽  
Vol 163 ◽  
pp. 762-775 ◽  
Author(s):  
Xiaoyang Chen ◽  
Hongwei Huo ◽  
Jun Huan ◽  
Jeffrey Scott Vitter
Keyword(s):  
Efficient Algorithm ◽  
Edit Distance ◽  
Graph Edit Distance ◽  
Distance Computation

scholarly journals Redefining the Graph Edit Distance

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Triangle Inequality ◽  
Edit Distance ◽  
Minimum Amount ◽  
Graph Edit Distance ◽  
Edit Operation ◽  
Attributed Graphs ◽  
Distance Properties

AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.

scholarly journals Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

2020 ◽  
Vol 20 (18) ◽  
pp. 1582-1592 ◽  
Author(s):  
Carlos Garcia-Hernandez ◽  
Alberto Fernández ◽  
Francesc Serratosa
Keyword(s):  
Virtual Screening ◽  
Edit Distance ◽  
Graph Matching ◽  
Optimization Techniques ◽  
Graph Edit Distance ◽  
Structure Activity ◽  
Future Drug ◽  
Minimum Number ◽  
Type Node ◽  
Activity Information

Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

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