Task-Oriented ML/DL Library Recommendation Based on a Knowledge Graph

AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement specific AI tasks. As application developers usually are not AI experts, they often choose to integrate existing implementations of ML/DL models as libraries for their AI tasks. As an active research area, AI attracts many researchers and produces a lot of papers every year. Many of the papers propose ML/DL models for specific tasks and provide their implementations. However, it is not easy for developers to find ML/DL libraries that are suitable for their tasks. The challenges lie in not only the fast development of AI application domains and techniques, but also the lack of detailed information of the libraries such as environmental dependencies and supporting resources. In this paper, we conduct an empirical study on ML/DL library seeking questions on Stack Overflow to understand the developers' requirements for ML/DL libraries. Based on the findings of the study, we propose a task-oriented ML/DL library recommendation approach, called MLTaskKG. It constructs a knowledge graph that captures AI tasks, ML/DL models, model implementations, repositories, and their relationships by extracting knowledge from different sources such as ML/DL resource websites, papers, ML/DL frameworks, and repositories. Based on the knowledge graph, MLTaskKG recommends ML/DL libraries for developers by matching their requirements on tasks, model characteristics, and implementation information. Our evaluation shows that 92.8% of the tuples sampled from the resulting knowledge graph are correct, demonstrating the high quality of the knowledge graph. A further experiment shows that MLTaskKG can help developers find suitable ML/DL libraries using 47.6% shorter time and with 68.4% higher satisfaction.