  In contract analysis and contract automation, a knowledge base (KB) of legal
entities is fundamental for performing tasks such as contract verification,
contract generation and contract analytic. However, such a KB does not always
exist nor can be produced in a short time. In this paper, we propose a
clustering-based approach to automatically generate a reliable knowledge base
of legal entities from given contracts without any supplemental references. The
proposed method is robust to different types of errors brought by
pre-processing such as Optical Character Recognition (OCR) and Named Entity
Recognition (NER), as well as editing errors such as typos. We evaluate our
method on a dataset that consists of 800 real contracts with various qualities
from 15 clients. Compared to the collected ground-truth data, our method is
able to recall 84\% of the knowledge.
