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MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph
BACKGROUND: Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734719/ https://www.ncbi.nlm.nih.gov/pubmed/33317504 http://dx.doi.org/10.1186/s12911-020-01134-w |
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author | Vandewiele, Gilles Steenwinckel, Bram Turck, Filip De Ongenae, Femke |
author_facet | Vandewiele, Gilles Steenwinckel, Bram Turck, Filip De Ongenae, Femke |
author_sort | Vandewiele, Gilles |
collection | PubMed |
description | BACKGROUND: Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care. METHODS: We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime. RESULTS: We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable. CONCLUSIONS: The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance. |
format | Online Article Text |
id | pubmed-7734719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77347192020-12-15 MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph Vandewiele, Gilles Steenwinckel, Bram Turck, Filip De Ongenae, Femke BMC Med Inform Decis Mak Research BACKGROUND: Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care. METHODS: We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime. RESULTS: We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable. CONCLUSIONS: The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance. BioMed Central 2020-12-14 /pmc/articles/PMC7734719/ /pubmed/33317504 http://dx.doi.org/10.1186/s12911-020-01134-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Vandewiele, Gilles Steenwinckel, Bram Turck, Filip De Ongenae, Femke MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title | MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title_full | MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title_fullStr | MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title_full_unstemmed | MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title_short | MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
title_sort | mindwalc: mining interpretable, discriminative walks for classification of nodes in a knowledge graph |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734719/ https://www.ncbi.nlm.nih.gov/pubmed/33317504 http://dx.doi.org/10.1186/s12911-020-01134-w |
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