Joint Inference for Informed End-to-End Knowledge Base Population

Knowledge Base Population ({KBP}) aims to populate structured databases with facts extracted from text, encompassing tasks such as named entity recognition, coreference resolution, relation extraction, and entity linking. Traditional pipeline-based approaches sequentially chain modular components, leading to error propagation and unidirectional information flow. Additionally, black-box components often lack transparency and interpretability. In this paper, we propose a probabilistic pipeline framework for joint inference in end-to-end {KBP}. Our approach enables globally consistent decision-making by integrating local component feedback and external background knowledge. A key advantage is its ability to seamlessly incorporate knowledge about pipeline components, ontology constraints, linguistic patterns, and corpus characteristics. We evaluate our framework on two core {KBP} tasks: exhaustive relation extraction and entity linking.