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KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources

Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory setting...

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Detalles Bibliográficos
Autores principales: Witschel, Hans Friedrich, Riesen, Kaspar, Grether, Loris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148211/
http://dx.doi.org/10.1007/978-3-030-45439-5_50
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author Witschel, Hans Friedrich
Riesen, Kaspar
Grether, Loris
author_facet Witschel, Hans Friedrich
Riesen, Kaspar
Grether, Loris
author_sort Witschel, Hans Friedrich
collection PubMed
description Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated.
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spelling pubmed-71482112020-04-13 KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources Witschel, Hans Friedrich Riesen, Kaspar Grether, Loris Advances in Information Retrieval Article Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated. 2020-03-17 /pmc/articles/PMC7148211/ http://dx.doi.org/10.1007/978-3-030-45439-5_50 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Witschel, Hans Friedrich
Riesen, Kaspar
Grether, Loris
KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title_full KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title_fullStr KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title_full_unstemmed KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title_short KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources
title_sort kvgr: a graph-based interface for explorative sequential question answering on heterogeneous information sources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148211/
http://dx.doi.org/10.1007/978-3-030-45439-5_50
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