A novel knowledge graph embedding based API recommendation method for Mashup development

Abstract

Web API is an efficient and cost-effective method for service-oriented software development, and Mashup is a popular technology which combines multiple services to create more powerful services to address the increasing complexity of business requirements and speed up the software development process. Here, accurate and efficient API recommendation is vital for successful Mashup development. Currently, many existing methods combine various technologies and adopt diverse features, which results in complex models at the cost of higher computational overhead but with very limited improvement on recommendation accuracy. To address such an issue, in this paper, we propose an unsupervised API recommendation method based on deep random walks on knowledge graph. Specifically, we first construct a refined knowledge graph utilizing Mashup-API co-invocation patterns and service category attributes, and then we learn implicit low-dimensional embedding representations of entities from truncated random walks by treating walks as the equivalent of sentences. Meanwhile, to improve the recommendation accuracy, we design an entity bias procedure to reflect different entity preference (namely API-based neighborhood or Mashup-based neighborhood). Finally, we estimate the relevance between Mashup requirements and the existing services (Mashups and APIs) to obtain the API recommendation list. Since the API recommendation results can be obtained through unsupervised feature learning, automatic API recommendation can be provided for Mashup developers in real time. Comprehensive experimental results on a real-world dataset demonstrate that our proposed method can outperform several state-of-the-art methods in both recommendation accuracy and efficiency.