High-Order-Modal Knowledge Graph Powered API Recommendation for Mashup Development
Beichen Hu, Xihao Xie, Junhao Shen, Jia Zhang, Sengdar J. Lee, Seungwon Lee · 2024
As increasingly more APIs are published on the Internet, effective API recommendation remains a challenge yet highly demanded for mashup developers. This paper formalizes API recommendation as an incremental context-aware recom-mendation problem starting from a set of descriptive words and a set of APIs selected to date, supported by a fine-grained mashup-oriented knowledge graph (MKG). In contrast to traditional knowledge graphs where nodes are coarse-grained entities, entity-and relationship-encapsulated features are extracted as first-class citizens in an MKG, so that implicit feature relationships can be turned into explicit structural relationships. Two models are trained to learn fine-grained API selection strategies through path type patterns in the MKG, starting from intended descriptions and APIs selected, respectively. Extensive experiments over real-world datasets have demonstrated the effectiveness of the method.