Difference between revisions of "AmelieGyrard"

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Machine-to-Machine Measurement (M3) is a framework to semantically annotate and easily interpret Internet of Things (IoT) data. M3 enables designing interoperable domain-specific or cross-domain Semantic Web of Things (SWoT) applications. Our main goals are to help developers for fast-prototyping of IoT applications and reducing the learning curve of integrating semantic web technologies. We mainly address semantic interoperability on sensor data, ontologies, reasoning on data to build Web of Things applications.
 
Machine-to-Machine Measurement (M3) is a framework to semantically annotate and easily interpret Internet of Things (IoT) data. M3 enables designing interoperable domain-specific or cross-domain Semantic Web of Things (SWoT) applications. Our main goals are to help developers for fast-prototyping of IoT applications and reducing the learning curve of integrating semantic web technologies. We mainly address semantic interoperability on sensor data, ontologies, reasoning on data to build Web of Things applications.
  
=== LOVIoT: Ontology Catalogue for Internet of Things (LOV4IoT) ===
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=== [http://lov4iot.appspot.com/ LOVIoT: Ontology Catalogue for Internet of Things (LOV4IoT)] ===
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LOV4IoT (Linked Open Vocabularies for Internet of Things) references numerous ontology-based IoT projects:
 +
* Almost 400 ontology-based projects for IoT, smart cities, etc.
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* Almost 20 domains relevant to IoT referenced such as building, smart grid, smart agriculture, smart transportation, healthcare, etc.
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* We provide the LOV4IoT ontology catalogue as an HTML view.
 +
* We also provide the LOV4IoT RDF dataset.
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* LOV4IoT is an extension of the LOV (linked Open Vocabularies) catalogue.
  
 
=== [http://perfectsemanticweb.appspot.com/ PerfectO: Semantic Web Best Practices] ===
 
=== [http://perfectsemanticweb.appspot.com/ PerfectO: Semantic Web Best Practices] ===

Revision as of 20:51, 28 January 2018

Postoctoral researcher at the Kno.e.sis Center, under the supervision of Prof. Dr. Amit Sheth).

Additional information


Research Projects @ Knoesis

Improving ontology quality designed within Knoesis projects.

KHealth projects @ Knoesis

Other projects @ Knoesis

Semantic Web of Things Research Projects (2012 - 2018)

M3 Semantic Web of Things Framework

Machine-to-Machine Measurement (M3) is a framework to semantically annotate and easily interpret Internet of Things (IoT) data. M3 enables designing interoperable domain-specific or cross-domain Semantic Web of Things (SWoT) applications. Our main goals are to help developers for fast-prototyping of IoT applications and reducing the learning curve of integrating semantic web technologies. We mainly address semantic interoperability on sensor data, ontologies, reasoning on data to build Web of Things applications.

LOVIoT: Ontology Catalogue for Internet of Things (LOV4IoT)

LOV4IoT (Linked Open Vocabularies for Internet of Things) references numerous ontology-based IoT projects:

  • Almost 400 ontology-based projects for IoT, smart cities, etc.
  • Almost 20 domains relevant to IoT referenced such as building, smart grid, smart agriculture, smart transportation, healthcare, etc.
  • We provide the LOV4IoT ontology catalogue as an HTML view.
  • We also provide the LOV4IoT RDF dataset.
  • LOV4IoT is an extension of the LOV (linked Open Vocabularies) catalogue.

PerfectO: Semantic Web Best Practices

PerfectO references, classifies and provides tools to encourage Semantic Web Best Practices to achieve Semantic Interoperability by focusing on ontology improvement.

SLOR: Sensor-based Lined Open Rules

Publications

Google Scholar Index

  • Citation count: 448 (January 2017)
  • H-index: 13 (January 2017)


Ph.D. Dissertation

Designing Cross-Domain Semantic Web of Things Applications

  • Amelie Gyrard
  • Committee: Christian BONNET(advisor), Karima BOUDAOUD (advisor), Bruno MARTIN, Jérôme EUZENAT, Oscar CORCHO, Payam BARNAGHI, Claude HARY, and Philippe BADIA
  • Telecom ParisTech, 2015

Awards and Recognition

Scientific Animations

Tutorials

Community Engagement

Program Committee Member

  • ESWC Resource Track 2018
  • WF-IoT 2018
  • ISPRS International Journal of Geo-Information 2017
  • MDPI Sensors Journal 2016 - 2017, Impact Factor: 2.677 (2016)
  • IEEE Internet Computing Journal - 2017
  • International Semantic Web Conference (ISWC) 2017 Resources track
  • SWIT workshop at ISWC2017
  • Extended Semantic Web Conference (ESWC) 2017 (Sub-reviewer)
  • SIS-IoT: Semantic Interoperability and Standardization in the IoT Workshop co-located with Semantics 2017 (Sub-reviewer)
  • FiCloud 2017 - The 5th International Conference on Future Internet of Things and Cloud
  • 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)
  • IEEE Communications Magazine 2016
  • IEEE World Forum on Internet of Things (WF-IoT) 2016 Conference
  • Semantic Web Journal 2016
  • International Journal on Semantic Web and Information Systems (IJSWIS) 2016
  • Information Systems Journal Elsevier 2016
  • IEEE Internet of Things Journal 2016
  • 12th International Wireless Communications & Mobile Computing Conference (IWCMC 2016)
  • 6th International Conference on Computer and Communication Technology (ICCCT 2015)
  • Insight Student Conference 2015

Education

Doctor of Philosophy (Ph.D.) in Computer Science and Engineering


Work Experience

Postdoctoral Researcher - Kno.e.sis Center, Wright State University
January 2018 - Present


Postdoctoral Researcher - Ecole des Mines de Saint-Etienne
December 2016 - December 2017 (1 year)


Postdoctoral Researcher - Insight Center for Data Analytics, National University of Galway, Ireland
May 2015 - November 2016 (1 year and 7 months)


PhD and Research Assistant - Eurecom, Wright State University
January 2012 - April 2015 (3 years and 4 months)

  • Semantic Web of Things with M3 [1].


Funding and Proposals