demonstration

EAGER: Explainable Question Answering Using Knowledge Graphs

Authors:

Morteza ZihayatAuthors Info & Claims

GRADES & NDA '23: Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

Article No.: 4, Pages 1 - 5

https://doi.org/10.1145/3594778.3594877

Published: 21 June 2023 Publication History

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Abstract

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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Cited By

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Kontos J(2023)Analysis Dialogs and Machine ConsciousnessChatbots - The AI-Driven Front-Line Services for Customers10.5772/intechopen.112476Online publication date: 13-Sep-2023
https://doi.org/10.5772/intechopen.112476
Lee AShankararaman VOuh E(2023)Vision Paper: Advancing of AI Explainability for the Use of ChatGPT in Government Agencies – Proposal of A 4-Step Framework2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386797(5852-5856)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386797

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Information & Contributors

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Published In

GRADES-NDA '23: Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

June 2023

61 pages

ISBN:9798400702013

DOI:10.1145/3594778

Program Chairs:
Olaf Hartig
Amazon Web Services & Linköping University, Sweden
,
Yuichi Yoshida
National Institute of Informatics, Japan

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2023

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Demonstration

Conference

GRADES & NDA '23

Sponsor:

SIGMOD

GRADES & NDA '23: 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

June 18, 2023

WA, Seattle, USA

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Overall Acceptance Rate 29 of 61 submissions, 48%

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Cited By

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Kontos J(2023)Analysis Dialogs and Machine ConsciousnessChatbots - The AI-Driven Front-Line Services for Customers10.5772/intechopen.112476Online publication date: 13-Sep-2023
https://doi.org/10.5772/intechopen.112476
Lee AShankararaman VOuh E(2023)Vision Paper: Advancing of AI Explainability for the Use of ChatGPT in Government Agencies – Proposal of A 4-Step Framework2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386797(5852-5856)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386797

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Knowledge Graph Question Answering with Ambiguous Query

Building Knowledge Subgraphs in Question Answering over Knowledge Graphs

Question Answering over Knowledge Graphs with Query Path Generation