A unified Link Prediction architecture applied on a novel heterogenous Knowledge Base
File(s)
Accepted version
Author(s)
Hilman, David
Serban, Ovidiu
Type
Journal Article
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.
Date Issued
2022-04
Date Acceptance
2022-01-14
Citation
Knowledge-Based Systems, 2022, 241, pp.1-17
ISSN
0950-7051
Publisher
Elsevier BV
Start Page
1
End Page
17
Journal / Book Title
Knowledge-Based Systems
Volume
241
Copyright Statement
© 2022 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S0950705122000661?via%3Dihub
Subjects
08 Information and Computing Sciences
15 Commerce, Management, Tourism and Services
17 Psychology and Cognitive Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Article Number
108228
Date Publish Online
2022-02-01