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Amelie Gyrard

Bio: Amelie Gyrard is an academic researcher from Wright State University. The author has contributed to research in topics: Semantic Web & Ontology (information science). The author has an hindex of 21, co-authored 59 publications receiving 1263 citations. Previous affiliations of Amelie Gyrard include Centre national de la recherche scientifique & National University of Ireland, Galway.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
12 Dec 2016
TL;DR: A model is presented that aims to achieve semantic interoperability among heterogeneous testbeds through the usage of semantic-based technologies within the EU H2020's FIESTA-IoT project, and takes inspiration from the Noy et al. methodology for reusing and interconnecting existing ontologies.
Abstract: After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology.

149 citations

Proceedings ArticleDOI
14 Dec 2015
TL;DR: This work synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT).
Abstract: We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.

99 citations

Proceedings ArticleDOI
06 Mar 2014
TL;DR: This work proposes a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M 2M applications.
Abstract: The Internet of Things, more specifically, the Machine-to-Machine (M2M) standard enables machines and devices such as sensors to communicate with each other without human intervention. The M2M devices provide a great deal of M2M data, mainly used for specific M2M applications such as weather forecasting, healthcare or building automation. Existing applications are domain-specific and use their own descriptions of devices and measurements. A major challenge is to combine M2M data provided by these heterogeneous domains and by different projects. It is really a difficult task to understand the meaning of the M2M data to later reason about them. We propose a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M2M applications. A proof-of-concept to validate our approach is published online (http://sensormeasurement.appspot.com/).

82 citations

Proceedings ArticleDOI
24 Aug 2015
TL;DR: The M3 framework is based on semantic web technologies to explicitly describe the meaning of sensor measurements in an unified way to ease the interpretation of sensor data and to combine domains.
Abstract: Internet of Things (IoT) applications are becoming more and more popular but not interoperable with each other. In this paper, we propose the Machine-to-Machine Measurement (M3) framework to: (1) build IoT applications, (2) assist users in interpreting sensor measurements, and (3) combine domains with each other. The M3 framework is based on semantic web technologies to explicitly describe the meaning of sensor measurements in an unified way to ease the interpretation of sensor data and to combine domains.

81 citations

Journal ArticleDOI
TL;DR: This work will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.
Abstract: Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

73 citations


Cited by
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Journal ArticleDOI
01 Apr 1988-Nature
TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

9,929 citations

Journal ArticleDOI
TL;DR: This paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing and describes worldwide movements in intelligent manufacturing.

1,602 citations

Journal ArticleDOI
TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
Abstract: In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

560 citations

Journal ArticleDOI
TL;DR: A comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms is performed and the key challenges in this topic is presented.
Abstract: In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

378 citations

Journal ArticleDOI
TL;DR: The field is reviewed from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies, which identify the open issues and provide an insight for future study areas for IoT researchers.
Abstract: Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

343 citations