Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph - Diao, Jin; Zhou, Zhangbing; Xue, Xiao; Zhao, Deng; Chen, Shengpeng (Frontiers in Genetics, 2022)
Constructing a novel bioinformatics workflow through reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of their activities (or services). However, social relations of workflows, including relations between services and their developers have not been explored extensively. In fact, current techniques describe invoking relations between services mostly, and they can hardly reveal implicit relations between services. To address this challenge, we propose a Social-aware Scientific workflow Knowledge Graph (S2KG) to capture common types of entities and various types of relations through analyzing the relevant information about bioinformatics workflows and their developers recorded in repositories. Through using attributes of entities such as credit and creation time, the union impact of several positive and negative links in S2KG is identified to evaluate the feasibility of workflow fragments construction. To facilitate the discovery of single services, a service invoking network is extracted form S2KG, and service communities are constructed accordingly. A bioinformatics workflow fragment discovery mechanism based on the Y en’s method is developed to discover appropriate fragments with respect to certain user’s requirements. Extensive experiments are conducted, where bioinformatics workflows publicly accessible at the myExperiment repository are adopted. Evaluation results show that our technique performs better than the state-of-the-art techniques in terms of the precision, recall, and F1.