A survey on knowledge graphs: Representation, acquisition, and applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …
represent structural relations between entities have become an increasingly popular …
A comprehensive survey on automatic knowledge graph construction
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …
knowledge. To this end, much effort has historically been spent extracting informative fact …
Unifying large language models and knowledge graphs: A roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …
field of natural language processing and artificial intelligence, due to their emergent ability …
Graphadapter: Tuning vision-language models with dual knowledge graph
Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …
of vision-language models (VLMs) under the low-data regime, where only a few additional …
Prodigy: Enabling in-context learning over graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse
downstream tasks by conditioning on prompt examples, without optimizing any parameters …
downstream tasks by conditioning on prompt examples, without optimizing any parameters …
Inductive relation prediction by subgraph reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …
operating on latent representations (ie, embeddings) of entities and relations. However …
A comprehensive overview of knowledge graph completion
T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …
downstream knowledge-aware tasks (such as recommendation and intelligent question …
Aligraph: A comprehensive graph neural network platform
An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …
which capture rich and complex relationship among potentially billions of elements. Graph …
Structure-augmented text representation learning for efficient knowledge graph completion
Human-curated knowledge graphs provide critical supportive information to various natural
language processing tasks, but these graphs are usually incomplete, urging auto …
language processing tasks, but these graphs are usually incomplete, urging auto …
Meta relational learning for few-shot link prediction in knowledge graphs
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-
based methods, effective for link prediction in KGs, perform poorly on relations that only …
based methods, effective for link prediction in KGs, perform poorly on relations that only …