research-article

Knowledge Graph Embedding for Link Prediction: A Comparative Analysis

Authors:

Denilson Barbosa,

Donatella Firmani,

Antonio Matinata,

Paolo MerialdoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 2

Article No.: 14, Pages 1 - 49

https://doi.org/10.1145/3424672

Published: 04 January 2021 Publication History

Abstract

Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.

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Index Terms

Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Data management systems
    1. Information integration

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 2

Survey Paper and Regular Papers

April 2021

524 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3446665

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 04 January 2021

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 January 2020

Published in TKDD Volume 15, Issue 2

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https://dl.acm.org/doi/10.1109/TKDE.2024.3486915
Zhang YWang HShen WPeng G(2025)DuAK: Reinforcement Learning-Based Knowledge Graph Reasoning for Steel Surface Defect DetectionIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.330758822(557-569)Online publication date: 2025
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https://doi.org/10.1016/j.physa.2025.130406
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