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.