On the role of knowledge graphs in explainable AI

Lécué, Freddy

doi:10.3233/sw-190374

Cited by 96 publications

(46 citation statements)

References 40 publications

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“…Similarly, plenty of implementation problems arise out of looking closely at the algorithm's different features in order to achieve real interpretability and explanation. Knowledge Graphs have been designed to capture knowledge from heterogeneous domains, making them a great candidate to achieve explanation in deep learning systems [124].…”

Section: And Explainability: a Machine Learning Zoo Mini-tour And Explainable Ai A Review Of Machine Learning Interpretability Methods Pamentioning

confidence: 99%

On Deep Research Problems in Deep Learning

Ulhaq¹

2021

Preprint

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The subject of deep learning has emerged in the last decade as one of the most promising approaches to machine learning. Today, certainly, much of the recent progress in artificial intelligence is due to it, but research challenges are still unresolved and remain open to the research community. This paper attempts to offer a comprehensive review of deep learning progress in active research frontiers. On the one side, by presenting a brief overview of deep learning success, we inspire researchers to work in deep learning. On the other hand, we examine a range of technical issues, and open research issues that we believe are relevant topics for exploratory research. As deep learning applies to various fields, we restrict this paper’s scope to visual recognition tasks to analyze these problems with a specific lens. However, these problems will be broadly applicable to other fields. It will make it easier for new researchers to recognize outstanding research problems in the deep learning domain.

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Section: And Explainability: a Machine Learning Zoo Mini-tour And Explainable Ai A Review Of Machine Learning Interpretability Methods Pamentioning

confidence: 99%

On Deep Research Problems in Deep Learning

Ulhaq¹

2021

Preprint

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“…In particular, for opaque machine learning processes such as neural networks and genetic algorithms, KGs can help document the provenance of the workflow and improve the interpretability of results. A key feature of KGs is their capability of defining groups or clusters and their associated attributes, which can be leveraged to add a semantic layer to many machine learning algorithms (Lecue, 2020). For example, by explicating typical attributes of instances in a subgroup, KGs can explain the grouping process in a machine learning process and demonstrate the meaning of results (Ristoski and Paulheim, 2016).…”

Section: Intelligent Geosciences Underpinned By Knowledge Graphsmentioning

confidence: 99%

Knowledge graph construction and application in geosciences: A review

Ma¹

2022

Preprint

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Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts rele-vant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geoscience KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar stud-ies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.

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“…Graph neural networks (GNN) are deep learning-based models working on the graph domain [10, 25, 26]. Their properties have been recently drawn the attention of the artificial intelligence research community given their high interpretability and as the only non-Euclidean models available in machine learning [27, 13]. The combination of graph theory and neural network elements have made GNNs one of the most promising tools to analyse complex systems in the graph domain.…”

Section: Design Of a Biomedical Digital Twinmentioning

confidence: 99%

Graph representation forecasting of patient’s medical conditions: towards a digital twin

Barbiero

Viñas

Lió

2020

Preprint

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Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients. The aim of this work is to present how the integration of machine learning approaches with mechanistic computational modelling could yield a reliable infrastructure to run probabilistic simulations where the entire organism is considered as a whole. Methods: We propose a general framework that composes advanced AI approaches and integrates mathematical modelling in order to provide a panoramic view over current and future physiological conditions. The proposed architecture is based on a graph neural network (GNNs) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GANs) providing a proof of concept of transcriptomic integrability. Results: We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data to drive local and global clinical parameters and derive future trajectories representing the evolution of the physiological state of the patient. Significance: We argue that the graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modelling with AI. We believe that this work represents a step forward towards a healthcare digital twin.

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On the role of knowledge graphs in explainable AI

Cited by 96 publications

References 40 publications

On Deep Research Problems in Deep Learning

On Deep Research Problems in Deep Learning

Knowledge graph construction and application in geosciences: A review

Graph representation forecasting of patient’s medical conditions: towards a digital twin

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