EAGER:使用知识图谱进行可解释的问题回答

Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2023-06-18 DOI:10.1145/3594778.3594877

Andrew Chai, Alireza Vezvaei, Lukasz Golab, M. Kargar, D. Srivastava, Jaroslaw Szlichta, Morteza Zihayat

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引用次数: 0

摘要

我们提出EAGER:一个回答用自然语言表达的问题的工具。EAGER的核心是一个模块化管道，用于在没有人工干预的情况下从原始文本生成知识图。值得注意的是，EAGER使用知识图来回答问题并解释推导答案背后的推理。我们的演示将展示自动知识图生成管道和可解释的问答功能。最后，我们概述了有待解决的问题和未来工作的方向。

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EAGER: Explainable Question Answering Using Knowledge Graphs

We present EAGER: a tool for answering questions expressed in natural language. Core to EAGER is a modular pipeline for generating a knowledge graph from raw text without human intervention. Notably, EAGER uses the knowledge graph to answer questions and to explain the reasoning behind the derivation of answers. Our demonstration will showcase both the automated knowledge graph generation pipeline and the explainable question answering functionality. Lastly, we outline open problems and directions for future work.

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Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

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