Large language models and knowledge graphs: Opportunities and challenges
Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
Machine knowledge: Creation and curation of comprehensive knowledge bases
Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
[HTML][HTML] A comprehensive survey of entity alignment for knowledge graphs
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 …
ease-of-store, recognizable, and understandable way for machines and provide a rich …
A comprehensive survey of graph neural networks for knowledge graphs
The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …
among entities of diverse classifications, has gradually become one of the critical tools for …
Multilingual knowledge graph completion with self-supervised adaptive graph alignment
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning
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 …
research community and the industry due to their essential role in AI applications. Entity …
A critical re-evaluation of neural methods for entity alignment
M Leone, S Huber, A Arora, A García-Durán… - Proceedings of the …, 2022 - dl.acm.org
Neural methods have become the de-facto choice for the vast majority of data analysis tasks,
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …
Graph matching for knowledge graph alignment using edge-coloring propagation
Abstract Knowledge graph (KG) is a kind of structured human knowledge of modeling the
relationships between real-world entities. High quality KG is of crucial importance for many …
relationships between real-world entities. High quality KG is of crucial importance for many …
Entity alignment with reliable path reasoning and relation-aware heterogeneous graph transformer
W Cai, W Ma, J Zhan, Y Jiang - arXiv preprint arXiv:2205.08806, 2022 - arxiv.org
Entity Alignment (EA) has attracted widespread attention in both academia and industry,
which aims to seek entities with same meanings from different Knowledge Graphs (KGs) …
which aims to seek entities with same meanings from different Knowledge Graphs (KGs) …
Interactive contrastive learning for self-supervised entity alignment
Self-supervised entity alignment (EA) aims to link equivalent entities across different
knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the …
knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the …