UPDATE-ENABLED TRIPLIFICATION OF RELATIONAL DATA INTO VIRTUAL RDF STORES

2010 ◽  
Vol 04 (04) ◽  
pp. 423-451 ◽  
Author(s):  
SUNITHA RAMANUJAM ◽  
VAIBHAV KHADILKAR ◽  
LATIFUR KHAN ◽  
MURAT KANTARCIOGLU ◽  
BHAVANI THURAISINGHAM ◽  
...  
Keyword(s):  
Semantic Web ◽  
Relational Database ◽  
Relational Databases ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Relational Data ◽  
Semantic Web Technologies ◽  
Heterogeneous Data Sources ◽  
Description Framework ◽  
Almost All

The current buzzword in the Internet community is the Semantic Web initiative proposed by the W3C to yield a Web that is more flexible and self-adapting. However, for the Semantic Web initiative to become a reality, heterogeneous data sources need to be integrated in order to enable access to them in a homogeneous manner. Since a vast majority of data currently resides in relational databases, integrating relational data sources with semantic web technologies is at the top of the list of activities required to realize the semantic web vision. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples; however almost all current work in this arena is uni-directional, presenting data from an underlying relational database into a corresponding virtual RDF store in a read-only manner. An enhancement over previous relational-to-RDF bridging work in the form of bi-directionality support is presented in this paper. The bi-directional bridge proposed here allows RDF data updates specified as triples to be propagated back into the underlying relational database as tuples. Towards this end, we present various algorithms to translate the triples to be updated/inserted/deleted into equivalent relational attributes/tuples whenever possible. Particular emphasis is laid, in this paper, on the translation and update propagation process for triples containing blank nodes and reification nodes, and a platform enhanced with our algorithms, called D2RQ++, through which bi-directional translation can be achieved, is presented.

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

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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