A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Last update: Nov 18, 2022

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Overview

Rce-KGQA

A novel pipeline framework for multi-hop complex KGQA task.

This framework mainly contains two modules, answering_filtering_module and relational_chain_reasoning_module

And this two module should be trained independently, at reference step, question and KG load into answering_filtering_module ad inputs, then get the top-K candidates ,and retrieval these candidates` relational chain in KG, and let relational_chain_reasoning_module provide the final answer to USERS.

overall pipeline architecture See model

answering_filtering_module See Module1

relational_chain_reasoning_module See Module2

Hope you enjoy it !!!

Owner

金伟强 -上海大学人工智能小渣渣~

@mention what can i do~

GitHub Repository

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A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Related tags

Overview

Rce-KGQA

Owner

金伟强 -上海大学人工智能小渣渣~

Create images and texts with the First Order Generative Adversarial Networks

Learn other languages using artificial intelligence with python.

[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS).

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A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Related tags

Overview

Rce-KGQA

Owner

金伟强 -上海大学人工智能小渣渣~

Create images and texts with the First Order Generative Adversarial Networks

Learn other languages ​​using artificial intelligence with python.

[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS).

An implementation of the "Attention is all you need" paper without extra bells and whistles, or difficult syntax

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

Kernel Point Convolutions

This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.

Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

Score refinement for confidence-based 3D multi-object tracking

REBEL: Relation Extraction By End-to-end Language generation

Semantic Image Synthesis with SPADE

Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

📚 Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

Official Implementation of "Transformers Can Do Bayesian Inference"

OpenAi's gym environment wrapper to vectorize them with Ray

Material related to the Principles of Cloud Computing course.

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Learn other languages using artificial intelligence with python.