Abstract Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

In the last few years, the interest in knowledge bases has grown exponentially in both the
research community and the industry due to their essential role in AI applications. Entity …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the
same real-world object. Recent advancement in KG embedding impels the advent of …

Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs
(KGs), which is essential in integrating multi-source KGs. Recently, with the introduction of …

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by
linking the equivalent entities from various KGs. GNN-based EA methods present promising …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

Entity alignment, aiming to identify equivalent entities across different knowledge graphs
(KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its …

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity
alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity …