Abstract
Link Prediction (LP) aims at addressing incompleteness of Knowledge Graph (KG). The goal of LP is to capture the distribution of entities and relations present in a KG and utilise these to predict probability of missing information. State-of-the-art LP approaches rely on latent feature models for this purpose. The research focus has predominantly been on application of LP to triple based datasets (e.g. Freebase, YAGO). However, with growing adoption of KGs, it is common to see more heterogeneous property graphs being used, examples of common properties are temporal and weight data. The contributions of the following work are two fold. First, we introduce a novel framework which is the first to provide support for latent feature model LP on heterogeneous Knowledge Bases (KBs). Second, we utilise a novel KB — Refinitiv Knowledge Graph, to produce a heterogeneous dataset with which capabilities of the framework are examined.
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