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 …
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 …
Threats, attacks, and defenses in machine unlearning: A survey
Recently, Machine Unlearning (MU) has gained considerable attention for its potential to
improve AI safety by removing the influence of specific data from trained Machine Learning …
improve AI safety by removing the influence of specific data from trained Machine Learning …
Graphcare: Enhancing healthcare predictions with personalized knowledge graphs
Clinical predictive models often rely on patients' electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …
integrating medical knowledge to enhance predictions and decision-making is challenging …
Contextualization distillation from large language model for knowledge graph completion
While textual information significantly enhances the performance of pre-trained language
models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing …
models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing …
CP-KGC: constrained-prompt knowledge graph completion with large language models
R Yang, L Fang, Y Zhou - arXiv preprint arXiv:2310.08279, 2023 - arxiv.org
Knowledge graph completion (KGC) aims to utilize existing knowledge to deduce and infer
missing connections within knowledge graphs. Text-based approaches, like SimKGC, have …
missing connections within knowledge graphs. Text-based approaches, like SimKGC, have …
Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement
The design and development of text-based knowledge graph completion (KGC) methods
leveraging textual entity descriptions are at the forefront of research. These methods involve …
leveraging textual entity descriptions are at the forefront of research. These methods involve …
Mocosa: Momentum contrast for knowledge graph completion with structure-augmented pre-trained language models
Knowledge Graph Completion (KGC) aims to conduct reasoning on the facts within
knowledge graphs and automatically infer missing links. Existing methods can mainly be …
knowledge graphs and automatically infer missing links. Existing methods can mainly be …
Large language model enhanced knowledge representation learning: A survey
The integration of Large Language Models (LLMs) with Knowledge Representation
Learning (KRL) signifies a pivotal advancement in the field of artificial intelligence …
Learning (KRL) signifies a pivotal advancement in the field of artificial intelligence …
Data-informed geometric space selection
Geometric representation learning (eg, hyperbolic and spherical geometry) has proven to be
efficacious in solving many intricate machine learning tasks. The fundamental challenge of …
efficacious in solving many intricate machine learning tasks. The fundamental challenge of …