Paper: Joint Inference for Knowledge Base Population

ACL ID D14-1205
Title Joint Inference for Knowledge Base Population
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

Populating Knowledge Base (KB) with new knowledge facts from reliable text re- sources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs. How- ever, the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors. In this paper, we propose a novel joint infer- ence framework to allow interactions be- tween the two subtasks and find an opti- mal assignment by addressing the coher- ence among preliminary local predictions: whether the types of entities meet the ex- pectations of relations explicitly or implic- itly, and whether the local predictions are globally compatible. We further measure the confidence of the extracted triples by looking at the details of the complete ex- traction proc...