OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Nevzorova, Olga; Zhiltsov, Nikita; Kirillovich, Alexander; Lipachev, Evgeny K.

doi:10.1007/978-3-319-11716-4_9

Cited by 30 publications

(10 citation statements)

References 19 publications

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“…We can also consider what has been done in scientific knowledge bases, such as Gene Ontology ( [1]), OntoMathPro ( [8]) or FMA ( [10]) that aim to represent the current state of our knowledge in a field. They generally consist of a terminology part that organizes very precisely the concepts of the domain and a part composed of assertions (statements) that represent our knowledge about these concepts.…”

Section: State Of the Artmentioning

confidence: 99%

An ontology for the formalization and visualization of scientific knowledge

Daponte,

Falquet

2021

Preprint

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The construction of an ontology of scientific knowledge objects, presented here, is part of the development of an approach oriented towards the visualization of scientific knowledge. It is motivated by the fact that the concepts that are used to organize scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of these ontologies is centered on this topic and presents them in a simple and easily understandable organization. This ontology has been constructed by 1) selecting concepts that appear in high level ontologies or in ontologies of knowledge objects of specific fields and 2) interviewing scientists in different fields. We have aligned this ontology with some of the sources used, which has allowed us to verify its consistency with respect to them. The validation of the ontology consists in using it to formalize knowledge from various sources, which we have begun to do in the field of physics.

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Section: State Of the Artmentioning

confidence: 99%

An ontology for the formalization and visualization of scientific knowledge

Daponte,

Falquet

2021

Preprint

View full text Add to dashboard Cite

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“…Additionally, the semantic structure of the mathematical calibration model was described using MathML [ 39 ], while the temporal data was represented using the XML “ ” datatype in the format “ ”, where Z refers to the time zone. The ontology extended the aforementioned model with concepts from the OntoMath

ontology [ 40 ] in order to model the mathematical details of the dynamic transfer behavior of sensors. The transfer behavior corresponding to the sensor calibration model is represented by the abstract class and its constituent mathematical elements were modeled using the class.…”

Section: Ontologies In Sensor Networkmentioning

confidence: 99%

Semantic Description of Quality of Data in Sensor Networks

Vedurmudi

Neumann

Gruber

et al. 2021

Sensors

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The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.

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“…The data required to represent the complete model for MSC 2010 are publicly available 8 . This approach inspired other projects tackling semantification of mathematics according to LOD principles, for example OntoMathPRO [10] or coli-conc [1]. The Encyclopedia of mathematics used to annotate its records with the SKOSified MSC 2010 via SPARQL query [11].…”

Section: Msc 2010 Skos Modelmentioning

confidence: 99%

10 Years Later: The Mathematics Subject Classification and Linked Open Data

Arndt¹,

Ion²,

Runnwerth³

et al. 2021

Preprint

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Ten years ago, the Mathematics Subject Classification MSC 2010 was released, and a corresponding machine-readable Linked Open Data collection was published using the Simple Knowledge Organization System (SKOS). Now, the new MSC 2020 is out. This paper recaps the last ten years of working on machine-readable MSC data and presents the new machine-readable MSC 2020. We describe the processing required to convert the version of record, as agreed by the editors of zbMATH and Mathematical Reviews, into the Linked Open Data form we call MSC2020-SKOS. The new form includes explicit marking of the changes from 2010 to 2020, some translations of English code descriptions into Chinese, Italian, and Russian, and extra material relating MSC to other mathematics classification efforts. We also outline future potential uses for MSC2020-SKOS in semantic indexing and sketch its embedding in a larger vision of scientific research data.

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OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Cited by 30 publications

References 19 publications

An ontology for the formalization and visualization of scientific knowledge

An ontology for the formalization and visualization of scientific knowledge

Semantic Description of Quality of Data in Sensor Networks

10 Years Later: The Mathematics Subject Classification and Linked Open Data

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