In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybri...
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressiv...
Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthc...
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based se...
Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (...
In knowledge graph representation learning, link prediction is among the most popular and influentia...
The ability of knowledge graphs to represent complex relationships at scale has led to their adoptio...
Embedding knowledge graphs is a common method used to encode information from the graph at hand proj...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
This thesis proposes a novel Knowledge Graph (KG) embedding model for Link Prediction (LP) for Knowl...
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. M...
Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowled...
Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, t...
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based se...
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressiv...
Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthc...
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based se...
Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (...
In knowledge graph representation learning, link prediction is among the most popular and influentia...
The ability of knowledge graphs to represent complex relationships at scale has led to their adoptio...
Embedding knowledge graphs is a common method used to encode information from the graph at hand proj...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
This thesis proposes a novel Knowledge Graph (KG) embedding model for Link Prediction (LP) for Knowl...
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. M...
Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowled...
Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, t...
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based se...
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressiv...
Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthc...
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based se...