The graph model is nowadays largely adopted to model a wide range of knowledge and
data, spanning from social networks to knowledge graphs (KGs), representing a successful …

This paper explores Text-to-Knowledge Graph (T2KG) construction „assessing Zero-Shot
Prompting (ZSP), Few-Shot Prompting (FSP), and Fine-Tuning (FT) methods with Large …

The existing knowledge graph embedding (KGE) models based on graph neural networks
(GNNs) typically aggregate unreliable neighboring node information, leading to a decrease …

Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in
data representation and their numerous applications, eg, vocabulary sharing, Q/A or …

This work presents a formalization of analogy on numbers that relies on generalized means.
It is motivated by recent advances in artificial intelligence and applications of machine …

Network threat detection has been challenging due to the complexities of attack activities
and the limitation of historical threat data to learn from. To help enhance the existing …

Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources,
and are nowadays covering an ever increasing array of domains. However, not all …

Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in
data representation and their numerous applications, eg, vocabulary sharing, Q/A or …

A recently-proposed framework for modeling analogies exploits tight relationships between
analogies and generalized means, and unifies different models of numerical analogies. In …

Recent years have seen a renewed interest in the potential of analogy detection and
analogical inference, with successful applications in Machine Learning (ML) to the retrieval …