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Title:
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NATURALMSEQUERIES - A NATURAL WAY TO QUERY MATERIAL SCIENCES ENGINEERING DATA EXPERIMENTS |
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Author(s):
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André Valdestilhas, Soudeh Javamasoudian, Ghezal Ahmad Jan Zia, Thilo Muth, Thomas Hanke and Horst Fellenberg |
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ISBN:
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978-989-8704-53-5 |
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Editors:
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Paula Miranda and Pedro Isaías |
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Year:
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2023 |
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Edition:
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Single |
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Keywords:
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Materials Science, Natural Language Processing, Semantic Web, Knowledge Graph, Predictions |
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Type:
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Full |
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First Page:
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125 |
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Last Page:
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132 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Materials science experiments involve complex data that are often very heterogeneous and challenging to reproduce. Challenges with materials science data were observed, for example, in a previous study on harnessing lightweight design potentials via the Materials Data Space for which the data from materials sciences engineering experiments were generated using linked open data principles, e.g., Resource Description Framework (RDF) as the standard model for data interchange on the Web. However, detailed knowledge of formulating questions in the query language SPARQL is necessary to query the data. It was noticed that domain experts in Materials Science lack knowledge of querying the data using SPARQL queries. With this work, we aim to develop NaturalMSEQueries an approach for the material science domain expert where instead of SPARQL queries, the user can develop expressions in natural language, e.g., English, to query the data. This will significantly improve the usability of Semantic Web approaches in materials science and lower the adoption threshold of the methods for the domain experts. We plan to evaluate our approach, with varying amounts of data, from different sources. Furthermore, we want to compare with synthetic data to assess the quality of the implementation of our approach. The repository is available online at https://github.com/Mat-O-Lab/KnowledgeUI. |
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