Document Type
Article
Source of Publication
IEEE Access
Publication Date
11-18-2024
Abstract
Knowledge graphs (KGs) possess a vital role in enhancing the semantic comprehension of extensive datasets across many fields. It facilitate activities like recommendation systems, semantic searching, and intelligent data mining. However, lacking information can sometimes limit the usefulness of knowledge graphs (KGs), as the lack of relationships between entities could severely limit their practical application. Most existing approaches for KG completion primarily concentrate on embedding-based methods or just use relational paths, neglecting the valuable structural information offered by node density. This research presents an approach that effectively combines relational paths and the density features of tail nodes to enhance the accuracy of predicting relationships that are missing in knowledge graphs. Our method combines the sequential relational context represented by paths with the structural prominence indicated by node density, allowing for a dual view on possible entity connections. We validate the effectiveness of our technique by conducting comprehensive tests on many benchmark datasets, revealing substantial enhancements compared to conventional approaches. The Dual-Rep model, which incorporates relational paths and node density features, has continuously shown improved performance across several metrics, such as Mean Reciprocal Rank (MRR), Hit at 1 (Hit@1), and Hit at 3 (Hit@3). The DualRep model achieved a mean reciprocal rank (MRR) of 90.80. Additionally, it achieved a hit rate of 87.39 at rank 1 (Hit@1) and a hit rate of 91.18.
DOI Link
ISSN
Disciplines
Computer Sciences
Keywords
Entity Embeddings, Entity Relationship Prediction, Graph Neural Networks, Graph Structural Features, Knowledge Graph Completion, Machine Learning in Knowledge Graphs, Node Density Analysis, Relational and Structural Dynamics, Relational Paths
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Gul, Haji; Al-Obeidat, Feras; Amin, Adnan; Wasim, Muhammad; and Moreira, Fernando, "DualRep: Knowledge Graph Completion by Utilizing Dual Representation of Relational Paths and Tail Node Density Insights" (2024). All Works. 6938.
https://zuscholars.zu.ac.ae/works/6938
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series