Kügler P, Marian M, Dorsch R, Schleich B, Wartzack S (2022)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2022
Book Volume: 10
Article Number: 18
Journal Issue: 2
DOI: 10.3390/lubricants10020018
Open Access Link: https://www.mdpi.com/2075-4442/10/2/18
Within the domain of tribology,
enterprises and research institutions are constantly working on new concepts,
materials, lubricants, or surface technologies for a wide range of
applications. This is also reflected in the continuously growing number of
publications, which in turn serve as guidance and benchmark for researchers and
developers. However, those are almost impossible to keep up with due to their
vast quantity and the associated complexity and diversity. Due to the lack of
suited data and knowledge bases, knowledge acquisition and aggregation is still
a manual process involving the time-consuming review of literature. Therefore, semantic
annotation and natural language processing (NLP) techniques can decrease this manual
effort by providing a semi-automatic support in knowledge acquisition. The generation
of knowledge graphs from textual sources promises improved reuse and retrieval
of information acquired from scientific literature in a structured way. Motivated
by this, the contribution introduces a novel semantic annotation pipeline for
generating knowledge graphs and streamline knowledge acquisition in the domain
of tribology. The pipeline is built on BERT, a state-of-the-art language model
and involves classic NLP tasks like information extraction, named entity
recognition and question answering.
APA:
Kügler, P., Marian, M., Dorsch, R., Schleich, B., & Wartzack, S. (2022). A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology. Lubricants, 10(2). https://doi.org/10.3390/lubricants10020018
MLA:
Kügler, Patricia, et al. "A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology." Lubricants 10.2 (2022).
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