OKGIT: Open Knowledge Graph Link Prediction with Implicit Types

TL;DR

This paper introduces OKGIT, a novel method for link prediction in open knowledge graphs that enhances prediction accuracy by incorporating type compatibility, addressing the issue of incompatible noun phrase predictions.

Contribution

The paper proposes a new type compatibility score and regularization technique to improve link prediction in OpenKGs, achieving state-of-the-art results.

Findings

OKGIT outperforms existing methods on multiple datasets.

It produces more type-compatible noun phrase predictions.

The approach significantly improves link prediction accuracy.

Abstract

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.