Big Semantic Data Processing in the Materials Design Domain

Lambrix, Patrick; Armiento, Rickard; Delin, Anna; Li, Huanyu

doi:10.1007/978-3-319-63962-8_293-1

Cited by 3 publications

(7 citation statements)

References 40 publications

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“…In total, ontology-based applications were observed in 14 of the selected papers. [28][29][30][32][33][34][35]37,38,40,41,[50][51][52] These applications include the reuse, combination, and extension of existing ontologies and semantic resources (Figure 3b,i,ii). An example is given by Li et al [33] demonstrating the extension of two nanotechnology ontologies with new concepts and axioms in a two-step procedure.…”

Section: Ontology Applicationmentioning

confidence: 99%

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The Intersection Between Semantic Web and Materials Science

Valdestilhas

Bayerlein

Torres

et al. 2023

Advanced Intelligent Systems

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The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re‐)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user‐friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data‐driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.

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Section: Ontology Applicationmentioning

confidence: 99%

“…We conclude this section by referring to other related works that present the wide potential of ontology application, RDF instance creation, and RDF information retrieval, for example, for domain knowledge representation and FAIR data management. [31,36,39,52]…”

Section: Rdf Ontology Application Instances Creation and Information ...mentioning

confidence: 99%

The Intersection Between Semantic Web and Materials Science

Valdestilhas

Bayerlein

Torres

et al. 2023

Advanced Intelligent Systems

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“…Further, a number of ontologies from the materials science domain focus on specific sub-domains (e.g., metals, ceramics, thermal properties, nanotechnology), and have been developed with a specific use in mind (e.g., search, data integration) [13]. For instance, the Materials Ontology [2] was developed for data exchange among thermal property databases, and MatOnto ontology [3] for oxygen ion conducting materials in the fuel cell domain, NanoParticle Ontology [20] represents properties of nanoparticles with the purpose of designing new nanoparticles, while the eNanoMapper ontology [10] focuses on assessing risks related to the use of nanomaterials from the engineering point of view.…”

Section: Ontologies and Standardsmentioning

confidence: 99%

“…Further, concepts from PROV-O are used. We use the metadata terms from the Dublin Core Metadata Initiative (DCMI) 13 to represent the metadata of MDO.…”

Section: The Development Of Mdomentioning

confidence: 99%

“…More and more researchers in the field of materials science have realized that data-driven techniques have the potential to accelerate the discovery and design of new materials. Therefore, a large number of research groups and communities have developed data-driven workflows, including data repositories (for an overview see [13]) and task-specific analytical tools. Materials design is a technological process with many applications.…”

Section: Introductionmentioning

confidence: 99%

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An Ontology for the Materials Design Domain

Armiento

Lambrix

2020

Lecture Notes in Computer Science

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In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.

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Ontology-based GraphQL server generation for data access and data integration

Li,

Hartig,

Armiento

et al. 2024

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In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.

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Big Semantic Data Processing in the Materials Design Domain

Cited by 3 publications

References 40 publications

The Intersection Between Semantic Web and Materials Science

The Intersection Between Semantic Web and Materials Science

An Ontology for the Materials Design Domain

Ontology-based GraphQL server generation for data access and data integration

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