Abstract
Recently a huge number of knowledge graphs (KGs) has been generated, but there has not been enough attention to generate high-quality metadata to enable users to reuse the KGs for their own purposes. The main challenge is to generate standardized and high quality descriptive metadata which helps users understand the content of the large KGs. Some existing solutions make use of a combination of schema-level patterns derived from graph summarization with instance-level snippets. I will follow this trend and develop a method based on a combination of content-based patterns with user activity data such as SPARQL query logs to make generated metadata more informative and useful than other developed approaches. The problem of current models is generating complex, long or insufficient metadata which I plan to tackle by proposing a guideline to generate standard metadata during my Ph.D.
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Acknowledgements
This research has been funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie project Knowgraphs (grant agreement ID: 860801). I would like to express my special thanks of gratitude to my advisors and collaborators Prof. Michel Dumontier, Prof Christopher Brewster, Dr. Remzi Celebi, Chang Sun and Vincent Emonet.
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Mohammadi, M. (2022). (Semi-) Automatic Construction of Knowledge Graph Metadata. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_32
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