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 …

Abstract Knowledge graph completion (KGC) aims to infer missing links between entities
based on the observed ones. Current KGC methods primarily focus on KG embedding …

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 …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

Pre-trained language models have demonstrated impressive performance in both natural
language processing and program understanding, which represent the input as a token …

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet
constructing them through human annotations can be costly. As a result, various automatic …

Composed image retrieval (CIR) is the task of searching target images using an image-text
pair as a query. Given the straightforward relation of query pair-target image, the dominant …

Abstract Knowledge graph embedding (KGE) is essential for various applications,
particularly in link prediction and other downstream tasks. While existing convolutional …

The integration of Large Language Models (LLMs) with Knowledge Representation
Learning (KRL) signifies a pivotal advancement in the field of artificial intelligence …

This paper presents a parameter-lite transfer learning approach of pretrained language
models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all …