Some thoughts on knowledge-enhanced machine learning

Cozman, Fábio Gagliardi; Munhoz, Hugo Neri

doi:10.1016/j.ijar.2021.06.003

Cited by 10 publications

(4 citation statements)

References 63 publications

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“…The knowledge-enhanced machine learning approach attracts less attention that the data-driven approaches. However, KRR is still key in developing the future generation of AI systems development [21], even Deep Neural Networks (DNN) can create KRR but just in a different form [22]. In our vision, the KRR should not only extract knowledge from data but also learn knowledge from system actions that can support the reasoning process.…”

Section: Knowledge Representation and Reasoningmentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

O'Neill¹,

Kermanizadeh²

2023

Preprint

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The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

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Section: Knowledge Representation and Reasoningmentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

O'Neill¹,

Kermanizadeh²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The knowledge-enhanced machine learning approach attracts less attention than the data-driven approaches. However, KRR is still key in developing the future generation of AI system development [22]; even Deep Neural Networks (DNNs) can create KRR, just in a different form [23]. In our vision, KRR should not only extract knowledge from data but also learn knowledge from system actions that can support the reasoning process.…”

Section: Knowledge Representation and Reasoningmentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

Yu,

O’Neill,

Kermanizadeh

2023

Bioengineering

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The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances.

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“…Moreover, understanding the model's rationale is a challenge in itself, as represented by the burgeoning field of explainable artificial intelligence [29,14,4]. So a careful delineation is needed as to which parts are automated, which parts are delegated to humans, which parts can be obtained from humans a-priori (i.e., so-called knowledge-enhanced machine learning [9]), but also how systems can be made to reason about their environment so that they are able to capture and deliberate on their choices, however limiting their awareness of the world might be. In the very least, the latter capacity offers an additional layer of protection, control and explanation before delegating, as the systems can point out which beliefs and observations led to their actions.…”

Section: Motivationmentioning

confidence: 99%

Tractable Probabilistic Models for Ethical AI

Belle

2022

Graph-Based Representation and Reasoning

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Among the many ethical dimensions that arise in the use of ML technology, three stand out as immediate and profound: enabling the interpretability of the underlying decision boundary, addressing the potential for learned algorithms to become biased against certain groups, and capturing blame and responsibility for a system's outcomes. In this talk, we advocate for a research program that seeks to bridge tractable (probabilistic) models for knowledge acquisition with rich models of autonomous agency that draw on philosophical notions of beliefs, intentions, causes and effects.

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Some thoughts on knowledge-enhanced machine learning

Cited by 10 publications

References 63 publications

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

Tractable Probabilistic Models for Ethical AI

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