We introduce the Collaborative Oriented Relation Extraction (CORE) system for Knowledge Base Construction, based on the combination of Relation Extraction (RE) methods and domain experts feedback. CORE features a seamless, transparent, and modular architecture that suits large-scale processing. Via active learning, the CORE system bootstraps Knowledge Bases (KBs) and then employs RE methods to scale to large text corpora. We employ CORE to build one of the largest KBs focusing on fine-grained gene expression- cancer associations, fundamental to complement and validate experimental data for precision medicine and cancer research. We conducted comprehensive experiments showing the robustness of the approach and highlighting the scalability of CORE to large text corpora with limited manual annotations.
Bootstrapping Gene Expression-Cancer Knowledge Bases with Limited Human Annotations
Marchesin S.;Menotti L.;Giachelle F.;Silvello G.;
2024
Abstract
We introduce the Collaborative Oriented Relation Extraction (CORE) system for Knowledge Base Construction, based on the combination of Relation Extraction (RE) methods and domain experts feedback. CORE features a seamless, transparent, and modular architecture that suits large-scale processing. Via active learning, the CORE system bootstraps Knowledge Bases (KBs) and then employs RE methods to scale to large text corpora. We employ CORE to build one of the largest KBs focusing on fine-grained gene expression- cancer associations, fundamental to complement and validate experimental data for precision medicine and cancer research. We conducted comprehensive experiments showing the robustness of the approach and highlighting the scalability of CORE to large text corpora with limited manual annotations.
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