Publication | Conference on Neural Information Processing Systems 2019

Relational Graph Representation Learning for Open-Domain Question Answering

“Knowledge graphs” formalize knowledge about a domain of interest. They can contain multimodal information about entities and represent complex interactions among objects or concepts within a domain. Unlike ontologies, knowledge graphs are compatible with deep learning framework and can automatically predict missing knowledge. Knowledge graph technology is vastly used in software companies such as Google to augment their search results, Amazon to give better recommendations, and also in more specialized domains such as healthcare. Autodesk has also a knowledge graph called “Design Graph” which represents the interactions between parts of an assembly. This research can show how to use knowledge graphs such as Design Graph to augment downstream tasks such as classification, recommendation, or question-answering. Moreover, considering the domain of this project (natural language processing), it can help with applications that include textual data such as automatic extraction of Building Codes.

Download publication


Relational Graph Representation Learning for Open-Domain Question Answering

Salvatore Vivona, Kaveh Hassani

Conference on Neural Information Processing Systems 2019

We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task. Our model can learn contextual representation by jointly learning and updating the query, knowledge graph, and document representations. The experiments suggest that our model achieves state-of-the-art on the WebQuestionsSP benchmark.

Related Resources



A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly

In this work we propose a learning approach to high-precision robotic…



An FPGA-based model suitable for evolution and development of spiking neural networks

We propose a digital neuron model suitable for evolving and growing…



Research Conversations with Vivian Liu

A former Autodesk Research intern shares her experiences and reflects…



Help Autodesk Create Products for a Better Future

The Autodesk Research Community invites customers to provide feedback…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us