Semantic Query on Materials Data Based on Mapping MatML to an OWL Ontology

Zhang, Xiaoming; Hu, Changjun; Li, Huayu

doi:10.2481/dsj.8.1

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…NOMAD Meta‐info, ESCDF, and OpenKIM are the first attempts to categorize computational results in atomistic materials science. PLINIUS is used in the field of ceramics, ONTORULE in the steel industry, SLACKS for laminated composites, and PIF, Ashino, EMMO, MatOnto, Premap, and MatOWL represent general materials science data. Although the development of these materials ontologies has accelerated, they are not nearly as mature as in other fields, for example, the biosciences .…”

Section: Materials Ontologymentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

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Section: Materials Ontologymentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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“…The prototype demonstrates how the use of Web Services in combination with the ELSSI-EMD data formats has enabled MatDB Online to integrate with testing facilities without the need to modify OEM software. (Ueno, Sakai, Sakaida, Isonishi, Shuto, & Oyatsu, 2011), ISO 10303-235 (2009), ISO 15926-2 (2003, Material Ontology V1.1 (Ashino & Fujita, 2006), MatOWL (Zhang, Hu, & Li, 2009), and the ISO terminology database at https://cdb.iso.org. Resources relevant to ICT Standards for reporting engineering materials data include the ECISS/TC 100 documentary Standards for reporting engineering materials data, such as EN 10204 (2004) andEN 10168 (2004).…”

Section: Compliance With Testing Standardsmentioning

confidence: 99%

MatDB Online - A Standards-Based System for Preserving, Managing, and Exchanging Engineering Materials Test Data

Austin

Over

2012

Data Sci. J.

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With ICT Standards playing a key role in support of research and development in many disciplines, the European Commission Institute for Energy and Transport is keen to promote the development and adoption of ICT Standards for engineering data. In this respect, its MatDB Online facility is a Standards-based system for preserving, managing, and exchanging engineering materials test data. While MatDB Online has evolved over more than 30 years to incorporate the latest innovations in data preservation and exchange, such as XML-based data transfer and data citation using digital object identifiers, it continues to rely on a robust data model developed more than 30 years ago through the joint efforts of the National Research Institute for Metals

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“…Linking to expert material information Research by Zhang et al [61] has targeted the conversion of MATML information into a MatOWL information, in order to make material data more easily accessible to material scientists, namely through semantic queries. Although this research originally focuses on material scientists, added value may be within reach also for AEC specialists when they have the means to connect certain building products to concrete expert material information and hence make this expert information available for calculations and simulations, including for instance a structural calculation of a steel structure.…”

Section: 2mentioning

confidence: 99%

Increasing information feed in the process of structural steel design

Pauwels¹,

Jonckheere²,

Meyer³

et al. 2012

IJSMED

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Research initiatives throughout history have shown how a designer typically makes associationsand references to a vast amount of knowledge based on experiences to make decisions. With theincreasing usage of information systems in our everyday lives, one might imagine an information systemthat provides designers access to the ‘architectural memories’ of other architectural designers during thedesign process, in addition to their own physical architectural memory. In this paper, we discuss how theincreased adoption of semantic web technologies might advance this idea. We investigate to what extentinformation can be described with these technologies in the context of structural steel design. Thisinvestigation indicates possibilities regarding information reuse in the process of structural steel design and,by extent, in other design contexts as well.

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Semantic Query on Materials Data Based on Mapping MatML to an OWL Ontology

Abstract: MatML plays an important role in materials data

Cited by 19 publications

References 17 publications

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Data‐Driven Materials Science: Status, Challenges, and Perspectives

MatDB Online - A Standards-Based System for Preserving, Managing, and Exchanging Engineering Materials Test Data

Increasing information feed in the process of structural steel design

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