6 accepted papers
Retrieval-augmented generation (RAG) is widely adopted for knowledge-intensive tasks, but unverified external knowledge can pose risks such as data injection and retrieval pollution, leading to unexpected generation. Existing defenses rely on patch-based fixes, which limit generalization and increas
The significance of Temporal Knowledge Graphs (TKGs) in Artificial Intelligence (AI) lies in their capacity to incorporate time-dimensional information, support complex reasoning and prediction, optimize decision-making processes, enhance the accuracy of recommendation systems, promote multimodal da…
Graph Neural Networks (GNNs) play a pivotal role in graph representation learning, addressing challenges across diverse applications. Despite their significance, data-driven GNNs often overlook biases, raising fairness concerns. Inspired by counterfactuals, we inquire, ’In graph data, how does remov…
Researchers have applied knowledge graph embedding (KGE) techniques with advanced neural network techniques, such as capsule networks, for predicting drug-drug interactions (DDIs) and achieved remarkable results. However, most ignore molecular structure and position features between drug pairs. They…
The proliferation of social media exacerbates information fragmentation, posing challenges to understanding public events. We address the problem of event reconstruction with a novel Multi-view Contrast Event Reconstruction (MCER) model. MCER maximizes feature dissimilarity between different views o…
Despite the significant advances in RGB-D scene recognition, there are several major limitations that need further investigation. For example, simply extracting modal-specific features neglects the complex relationships among multiple modalities of features. Moreover, cross-modal features have not b…