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Found 7 papers, 4 papers with code
From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
The conventional process of building Ontologies and Knowledge Graphs (KGs) heavily relies on human domain experts to define entities and relationship types, establish hierarchies, maintain relevance to the domain, fill the ABox (or populate with instances), and ensure data quality (including amongst others accuracy and completeness).
Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review
The ability to replicate and validate KGs is fundamental for ensuring the trustworthiness and sustainability of the knowledge they represent.
Concept explainability for plant diseases classification
Plant diseases remain a considerable threat to food security and agricultural sustainability.
BiodivTab: Semantic Table Annotation Benchmark Construction, Analysis, and New Additions
Individual cells and columns are assigned to KG entities and classes to disambiguate their meaning.
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Machine learning (ML) is an increasingly important scientific tool supporting decision making and knowledge generation in numerous fields.
Towards Building Knowledge by Merging Multiple Ontologies with CoMerger: A Partitioning-based Approach
Ontologies are the prime way of organizing data in the Semantic Web.
Dataset Search In Biodiversity Research: Do Metadata In Data Repositories Reflect Scholarly Information Needs?
We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled.
