Causal Question Answering (CQA) is essential for knowledge discovery, focusing on the intricate dynamics between events and entities without predefined contexts. Despite advancements of CQA models through Knowledge Graphs (KGs) and Pre-Trained Language Models (PLMs), existing approaches are hindered by knowledge conflict, insufficient capacity, and limitations in information fusion. Large Language Models (LLMs) have significantly improved natural language understanding and reasoning but often suffer from causal hallucinations. To address these challenges, we introduce KLop, a framework that aligns representations of Causal Knowledge Graph (CKG) and Large Language Models for CQA. KLop pre-trains a graph embedding model for entity embedding and uses a frozen LLM for text embedding. The main components of KLop are the descriptor module and the aligner module. The descriptor leverages descriptive texts generated by LLMs to create training data for knowledge alignment, while the aligner utilizes self-attention to train query tokens for modality alignment. Experiments on public CQA datasets validate that KLop outperforms various advanced baselines in reasoning accuracy, as well as achieving causal knowledge integration and joint reasoning.

Compressed sensing (CS) has been successfully applied to realize image reconstruction. Neural networks have been introduced to the CS of images to exploit the prior known support information, which can improve the reconstruction quality. Capsule Network (Caps Net) is the latest achievement in neural networks, and can well represent the instantiation parameters of a specific type of entity or part of an object. This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework. The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest. To lead the dynamic routing to the most likely index, a group of prediction vectors is designed determined by the information. Furthermore, the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms. It is concluded that the proposed capsule network (Caps Net) creates higher reconstruction quality at nearly the same time with traditional Caps Net.