Abstract
Increasing physical objects connected to the Internet make it possible for smart things to access all kinds of cloud services. Mashup has been an effective way to the rapid IoT (Internet of Things) application development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of mashup services. This paper proposes a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.
Highlights
Internet of ings (IoT) was firstly introduced to the community in 1999 for supply chain management
We will arrive to the post-cloud era, where there will be large amounts of smart things to access all kinds of cloud services, and the capabilities of smart things can be enhanced by interacting with other functional entities through the interfaces of cloud services
NLP techniques are increasingly applied in the software engineering domain, which have been shown to be useful in requirements engineering [3], usability of Application Programming Interfaces (APIs) documents [4], and other areas [5]. us, semantic queries may get more accurate results
Summary
Internet of ings (IoT) was firstly introduced to the community in 1999 for supply chain management. (1) We propose a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network (2) We conduct comprehensive experiments on a realworld dataset, demonstrating the effectiveness of our approach e remainder of this paper is organized as follows: Section 2 gives a motivating scenario and presents our approach. We propose a simple but effective approach AMSRSSF to rank mashup services by using semantic and structural features It applies a two-mode graph to describe mashups, web APIs, and their relations formally. Our work considers the task of recommendation via representation learning as follows: given a requirement q described in multidimensional information, which includes semantic and structural features, the corresponding recommended services are given via similarity calculation, which exists in terms of the representation learning model.
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