摘要
The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.
The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.
基金
Supported by the National Natural Science Foundation of China(No. 61070156)
Special Youth Research and Innovation Programs (Nos.2009QNA5025 and 2010QNA5044)
IBM-ZJU Joint Research Projects
