Federated graph machine learning: A survey of concepts, techniques, and applications
Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …
Linkbert: Pretraining language models with document links
M Yasunaga, J Leskovec, P Liang - ar**
downstream tasks. However, existing methods such as BERT model a single document, and …
downstream tasks. However, existing methods such as BERT model a single document, and …
QA-GNN: Reasoning with language models and knowledge graphs for question answering
The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
Neural bellman-ford networks: A general graph neural network framework for link prediction
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …
methods, in this paper we propose a general and flexible representation learning framework …
Making large language models perform better in knowledge graph completion
Large language model (LLM) based knowledge graph completion (KGC) aims to predict the
missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to …
missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to …
Reasoning on graphs: Faithful and interpretable large language model reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
A survey on knowledge graph embeddings for link prediction
Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …
in information retrieval, natural language processing, recommendation systems, etc …
Beta embeddings for multi-hop logical reasoning in knowledge graphs
One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop
logical reasoning over the facts captured by a knowledge graph (KG). This problem is …
logical reasoning over the facts captured by a knowledge graph (KG). This problem is …
Colake: Contextualized language and knowledge embedding
With the emerging branch of incorporating factual knowledge into pre-trained language
models such as BERT, most existing models consider shallow, static, and separately pre …
models such as BERT, most existing models consider shallow, static, and separately pre …