  Open Knowledge Graphs (OpenKG) refer to a set of (head noun phrase, relation
phrase, tail noun phrase) triples such as (tesla, return to, new york)
extracted from a corpus using OpenIE tools. While OpenKGs are easy to bootstrap
for a domain, they are very sparse and far from being directly usable in an end
task. Therefore, the task of predicting new facts, i.e., link prediction,
becomes an important step while using these graphs in downstream tasks such as
text comprehension, question answering, and web search query recommendation.
Learning embeddings for OpenKGs is one approach for link prediction that has
received some attention lately. However, on careful examination, we found that
current OpenKG link prediction algorithms often predict noun phrases (NPs) with
incompatible types for given noun and relation phrases. We address this problem
in this work and propose OKGIT that improves OpenKG link prediction using novel
type compatibility score and type regularization. With extensive experiments on
multiple datasets, we show that the proposed method achieves state-of-the-art
performance while producing type compatible NPs in the link prediction task.
