Combining Artificial Intelligence, Ontology, and Frequency-based Approaches to Recommend Activities in Scientific Workflows




recommendation system, scientific workflows, artificial intelligence, ontology


The number of activities provided by scientific workflow management systems is large, which requires scientists to know many of them to take advantage of the reusability of these systems. To minimize this problem, the literature presents some techniques to recommend activities during the scientific workflow construction. In this paper we specified and developed a hybrid activity recommendation system considering information on frequency, input and outputs of activities and ontological annotations. Additionally, this paper presents a modeling of activities recommendation as a classification problem, tested using 5 classifiers; 5 regressors; and a composite approach which uses a Support Vector Machine (SVM) classifier, combining the results of other classifiers and regressors to recommend; and Rotation Forest, an ensemble of classifiers. The proposed technique was compared to related techniques and to classifiers and regressors, using 10-fold-cross-validation, achieving a Mean Reciprocal Rank (MRR) at least 70% greater than those obtained by classical techniques.


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Author Biographies

Adilson Lopes Khouri, Universidade de São Paulo

Possui graduação em Sistemas de Informação pela Universidade do Estado de São Paulo (2011) e mestrado pela Universidade do Estado de São Paulo (2016). Desde 2009 atua como desenvolvedor de software nas plataformas .NET e Java. Sua Linha de pesquisa é inteligência artificial aplicada em bioinformática.

Luciano Antonio Digiampietri, Universidade de São Paulo

Possui graduação em Ciência da Computação pela Universidade Estadual de Campinas (2002), doutorado em Ciência da Computação pela Universidade Estadual de Campinas (2007) e o título de Livre-docente em Informação e Tecnologia pela USP (2015). Desde abril de 2008 é professor pesquisador no Bacharelado em Sistemas de Informação na Escola de Artes, Ciências e Humanidades da Universidade de São Paulo (EACH-USP) e desde 2010 é professor permanente no Mestrado em Sistemas de Informação da EACH-USP. Tem experiência na área de Ciência da Computação, com ênfase em Biologia Computacional, Bancos de Dados e Inteligência Artificial, atuando principalmente nos seguintes temas: workflows científicos, bioinformática, composição automática de serviços, processamento de imagens e análise de redes sociais.


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How to Cite

Khouri, A. L., & Digiampietri, L. A. (2018). Combining Artificial Intelligence, Ontology, and Frequency-based Approaches to Recommend Activities in Scientific Workflows. Revista De Informática Teórica E Aplicada, 25(1), 39–47.



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