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Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding

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Mori KurokawaAuthors Info & Claims

IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

Pages 172 - 176

https://doi.org/10.1145/3502223.3502248

Published: 24 January 2022 Publication History

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Abstract

Knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning framework which combines multiple knowledge graph embedding techniques with corresponding explainable AI techniques. Experiments using the third knowledge graph reasoning challenge dataset demonstrate the effectiveness of the framework.

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Cited By

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Qiu PPang LLuo YLiu YXing HLiu KZhuang G(2024)Earthquake event knowledge graph construction and reasoningGeomatics, Natural Hazards and Risk10.1080/19475705.2024.238376815:1Online publication date: 7-Aug-2024
https://doi.org/10.1080/19475705.2024.2383768
Qian YSun FWang XPan L(2024)TODEARInformation Sciences: an International Journal10.1016/j.ins.2024.121066679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121066
Egami SUgai TOota MMatsushita KKawamura TKozaki KFukuda K(2023)RDF-star2Vec: RDF-star Graph Embeddings for Data MiningIEEE Access10.1109/ACCESS.2023.334102911(142030-142042)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3341029

Index Terms

Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure ...

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IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

December 2021

204 pages

ISBN:9781450395656

DOI:10.1145/3502223

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 January 2022

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IJCKG'21

IJCKG'21: The 10th International Joint Conference on Knowledge Graphs

December 6 - 8, 2021

Virtual Event, Thailand

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Qiu PPang LLuo YLiu YXing HLiu KZhuang G(2024)Earthquake event knowledge graph construction and reasoningGeomatics, Natural Hazards and Risk10.1080/19475705.2024.238376815:1Online publication date: 7-Aug-2024
https://doi.org/10.1080/19475705.2024.2383768
Qian YSun FWang XPan L(2024)TODEARInformation Sciences: an International Journal10.1016/j.ins.2024.121066679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121066
Egami SUgai TOota MMatsushita KKawamura TKozaki KFukuda K(2023)RDF-star2Vec: RDF-star Graph Embeddings for Data MiningIEEE Access10.1109/ACCESS.2023.334102911(142030-142042)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3341029

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FedE: Embedding Knowledge Graphs in Federated Setting

Overview of knowledge reasoning for knowledge graph

Structured query construction via knowledge graph embedding

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