{"id":39126,"date":"2026-08-21T15:30:36","date_gmt":"2026-08-21T15:30:36","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/joint-inference-for-informed-end-to-end-knowledge-base-population\/"},"modified":"2026-09-17T11:21:03","modified_gmt":"2026-09-17T11:21:03","slug":"joint-inference-for-informed-end-to-end-knowledge-base-population","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/joint-inference-for-informed-end-to-end-knowledge-base-population\/","title":{"rendered":"Joint Inference for\u00a0Informed End-to-End Knowledge Base Population"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-39126","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/39126","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/39126\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=39126"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=39126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}