  Open attribute value extraction for emerging entities is an important but
challenging task. A lot of previous works formulate the problem as a
\textit{question-answering} (QA) task. While the collections of articles from
web corpus provide updated information about the emerging entities, the
retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers.
Effectively filtering out noisy articles as well as bad answers is the key to
improving extraction accuracy. Knowledge graph (KG), which contains rich, well
organized information about entities, provides a good resource to address the
challenge. In this work, we propose a knowledge-guided reinforcement learning
(RL) framework for open attribute value extraction. Informed by relevant
knowledge in KG, we trained a deep Q-network to sequentially compare extracted
answers to improve extraction accuracy. The proposed framework is applicable to
different information extraction system. Our experimental results show that our
method outperforms the baselines by 16.5 - 27.8\%.
