  Fine-grained Entity Typing is a tough task which suffers from noise samples
extracted from distant supervision. Thousands of manually annotated samples can
achieve greater performance than millions of samples generated by the previous
distant supervision method. Whereas, it's hard for human beings to
differentiate and memorize thousands of types, thus making large-scale human
labeling hardly possible. In this paper, we introduce a Knowledge-Constraint
Typing Annotation Tool (KCAT), which is efficient for fine-grained entity
typing annotation. KCAT reduces the size of candidate types to an acceptable
range for human beings through entity linking and provides a Multi-step Typing
scheme to revise the entity linking result. Moreover, KCAT provides an
efficient Annotator Client to accelerate the annotation process and a
comprehensive Manager Module to analyse crowdsourcing annotations. Experiment
shows that KCAT can significantly improve annotation efficiency, the time
consumption increases slowly as the size of type set expands.
