Knowledge graphs meet multi-modal learning: A comprehensive survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
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 …
Walklm: A uniform language model fine-tuning framework for attributed graph embedding
Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …
predictions in various real-world applications. However, real-world graphs nowadays are …
Enhancing emergency decision-making with knowledge graphs and large language models
Emergency management urgently requires comprehensive knowledge while having a high
possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence (AI) …
possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence (AI) …
Multi-level contrastive learning for script-based character understanding
In this work, we tackle the scenario of understanding characters in scripts, which aims to
learn the characters' personalities and identities from their utterances. We begin by …
learn the characters' personalities and identities from their utterances. We begin by …
Finetuning generative large language models with discrimination instructions for knowledge graph completion
Traditional knowledge graph (KG) completion models learn embeddings to predict missing
facts. Recent works attempt to complete KGs in a text-generation manner with large …
facts. Recent works attempt to complete KGs in a text-generation manner with large …
Graph machine learning in the era of large language models (llms)
Graphs play an important role in representing complex relationships in various domains like
social networks, knowledge graphs, and molecular discovery. With the advent of deep …
social networks, knowledge graphs, and molecular discovery. With the advent of deep …
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
Due to the presence of the natural gap between Knowledge Graph (KG) structures and the
natural language, the effective integration of holistic structural information of KGs with Large …
natural language, the effective integration of holistic structural information of KGs with Large …
From local to global: A graph rag approach to query-focused summarization
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an
external knowledge source enables large language models (LLMs) to answer questions …
external knowledge source enables large language models (LLMs) to answer questions …
Kg-retriever: Efficient knowledge indexing for retrieval-augmented large language models
Large language models with retrieval-augmented generation encounter a pivotal challenge
in intricate retrieval tasks, eg, multi-hop question answering, which requires the model to …
in intricate retrieval tasks, eg, multi-hop question answering, which requires the model to …