Search Results for author:
Found 14 papers, 5 papers with code
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs.
Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
Our research evaluates the adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT marginally improves adversarial robustness against existing attack methods.
Text2Loc: 3D Point Cloud Localization from Natural Language
We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text.
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
Exploring Link Prediction over Hyper-Relational Temporal Knowledge Graphs Enhanced with Time-Invariant Relational Knowledge
We develop two new benchmark hyper-relational TKG (HTKG) datasets, i. e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models both temporal facts and qualifiers.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i. e., interpolated and extrapolated link prediction, to the one-shot setting.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time.
Temporal Knowledge Graph Forecasting with Neural ODE
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
