Large language models and knowledge graphs: Opportunities and challenges
Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
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 …
SimKGC: Simple contrastive knowledge graph completion with pre-trained language models
Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
[PDF][PDF] Knowledge Graph Embedding: An Overview
Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …
InGram: Inductive knowledge graph embedding via relation graphs
Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …
missing triplets between new entities that are not observed during training. While most …
Fusing topology contexts and logical rules in language models for knowledge graph completion
Abstract Knowledge graph completion (KGC) aims to infer missing facts based on the
observed ones, which is significant for many downstream applications. Given the success of …
observed ones, which is significant for many downstream applications. Given the success of …
Combining prompt learning with contextual semantics for inductive relation prediction
Inductive relation prediction for knowledge graphs aims to predict missing relations between
two new entities. Most previous studies on relation prediction are limited to the transductive …
two new entities. Most previous studies on relation prediction are limited to the transductive …
Kglm: Integrating knowledge graph structure in language models for link prediction
J Youn, I Tagkopoulos - arXiv preprint arXiv:2211.02744, 2022 - arxiv.org
The ability of knowledge graphs to represent complex relationships at scale has led to their
adoption for various needs including knowledge representation, question-answering, and …
adoption for various needs including knowledge representation, question-answering, and …
Zero-shot and few-shot learning with knowledge graphs: A comprehensive survey
Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …
many of them often rely on a number of labeled samples for supervision. As sufficient …
Generalizing to unseen elements: A survey on knowledge extrapolation for knowledge graphs
Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered increasing …
applications, and knowledge graph embedding (KGE) methods have garnered increasing …