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Joint Inference for Knowledge Base Population
BibTeX
@MISC{Chen_jointinference,
author = {Liwei Chen and Yansong Feng and Jinghui Mo and Songfang Huang and Dongyan Zhao},
title = {Joint Inference for Knowledge Base Population},
year = {}
}
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Abstract
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 process. Experiments show that the proposed framework can significantly reduce the error propagations thus obtain more reliable facts, and outperforms com-petitive baselines with state-of-the-art re-lation extraction models. 1
Keyphrases
knowledge base population joint inference error propagation extracted triple reliable fact name mention local prediction novel joint infer-ence framework opti-mal assignment preliminary local prediction state-of-the-art re-lation extraction model new knowledge fact reliable text re-sources entity pair com-petitive baseline complete ex-traction process relation extractor upstream entity linkers knowledge base kb entity
