A Machine Learning Ontology

Braga, Julião; Dias, Joaquim L R; Regateiro, Francisco

doi:10.31226/osf.io/rc954

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…Therefore, the AIMS ontology needs to be defined by modifying current machine learning ontology standards. Researchers have realised that there is a need to have a machine learning ontology and some recent proposals in this domain are: the Machine Learning Schema and Ontologies (MLSO) introduces twenty-two top-layer concepts and four categories of lower-layer vocabularies (the detailed ontology design is in [35]); the Machine Learning Ontology (MLO) proposes to describe machine learning algorithms with seven top layer concepts of Algorithm, Application, Dependencies, Dictionary, Frameworks, Involved, and MLTypes [36].…”

Section: Services and Machine Learning Ontologiesmentioning

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: Services and Machine Learning Ontologiesmentioning

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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“…To obtain an ontology that would describe NLP quite fully, it is necessary to process a lot scientific papers and information resources from modeling field. To simplify and speed up this process, approaches are being developed to automate replenishment of ontology basing on NLP [1], [2] and modern web resources [3].…”

Section: Introductionmentioning

confidence: 99%

Development of an intelligent information resource model based on modern natural language processing methods

Sadirmekova

Sambetbayeva

Serikbayeva

et al. 2023

IJECE

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<span lang="EN-US">Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.</span>

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“…However, these works have several limitations. For instance, most of them, [1,2,7,10,14], do not describe well the data sets used to train and test the models. In addition, the majority of the works, [7,10,11,14], does not take into account model application domain and model operational performance.…”

Section: Introductionmentioning

confidence: 99%

SML: Semantic Machine Learning Model Ontology

Kallab,

Mansour,

Chbeir

2023

Lecture Notes in Computer Science

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Artificial Intelligence is a set of technologies that simulate human-like cognition, using computer software and systems, to perform tasks associated with intelligent beings. One method of doing so, is Machine Learning (ML), which enhances system efficiency based on learning algorithms that create models from data and its underlying patterns. Nowadays, many ML models are being generated with different characteristics (e.g., type of the algorithm used, data set used to train it, resulting model performance), thus making the selection of a suitable model for a given use case a complex task, especially for non-expert users (with no or limited knowledge in ML). In this paper, we propose SML, an ontology-based model for Semantic Machine Learning description. SML allows, mainly, to describe and store ML models' characteristics with their operational specifications, related data features, contextual usage, and evaluation metrics/scores to facilitate and improve ML model selection. The conducted experiments show promising results on both the efficiency and the performance levels.

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A Machine Learning Ontology

Cited by 8 publications

References 20 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

Development of an intelligent information resource model based on modern natural language processing methods

SML: Semantic Machine Learning Model Ontology

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