scholarly journals An Iterative Automatic Final Alignment Method in the Ontology Matching System

2018 ◽  
Vol 42 (1) ◽  
pp. 39-61 ◽  
Author(s):  
Marko Gulić ◽  
Marin Vuković
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
Alignment Method ◽  
Automatic Adjustment ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Final Alignment

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

scholarly journals Alin: improving interactive ontology matching by interactively revising mapping suggestions

2020 ◽  
Vol 35 ◽  
Author(s):  
Jomar Da Silva ◽  
Kate Revoredo ◽  
Fernanda Baião ◽  
Jérôme Euzenat
Keyword(s):  
State Of The Art ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
New Techniques ◽  
Domain Experts ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Expert Answer ◽  
Comparable Quality

Abstract Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

scholarly journals A multi-agent conversational system with heterogeneous data sources access

2016 ◽  
Vol 53 ◽  
pp. 172-191 ◽  
Author(s):  
Eduardo M. Eisman ◽  
María Navarro ◽  
Juan Luis Castro
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Multi Agent

Matching and integration across heterogeneous data sources

2006 ◽  
Author(s):  
Patrick Pantel ◽  
Andrew Philpot ◽  
Eduard Hovy
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals Prediction of RNA subcellular localization: learning from heterogeneous data sources

iScience ◽  
2021 ◽  
pp. 103298
Author(s):  
Anca Flavia Savulescu ◽  
Emmanuel Bouilhol ◽  
Nicolas Beaume ◽  
Macha Nikolski
Keyword(s):  
Subcellular Localization ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals Learning from heterogeneous data sources: an application in spatial proteomics

2015 ◽  
Author(s):  
Lisa M. Breckels ◽  
Sean Holden ◽  
David Wojnar ◽  
Claire M. Mulvey ◽  
Andy Christoforou ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Support Vector Machine ◽  
Transfer Learning ◽  
High Throughput ◽  
Cell Biology ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Support Vector ◽  
Proteomics Data ◽  
Heterogeneous Data Sources

AbstractSub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.AbbreviationsLOPITLocalisation of Organelle Proteins by Isotope TaggingPCPProtein Correlation ProfilingMLMachine learningTLTransfer learningSVMSupport vector machinePCAPrincipal component analysisGOGene OntologyCCCellular compartmentiTRAQIsobaric tags for relative and absolute quantitationTMTTandem mass tagsMSMass spectrometry

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