Yuxiao Dong

A Self-supervised Method for Entity Alignment

2021 · arXiv (Cornell University)

Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing large-scale KGs. Over the course of its development, supervision has been considered necessary for accurate alignments. Inspired …

EvoKG

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from …

LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA

2024 · arXiv (Cornell University)

Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their …

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

2019

We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing …

Heterogeneous Graph Transformer

2020

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, …

GCC

2020

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior …