Ontology-driven mashup auto-completion on a data API network
Chunying Zhou, Huajun Chen, Zhipeng Peng, Yuan Ni, Guotong Xie · Tsinghua Science & Technology · 2010
The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APls 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 APls. 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 precisionpof about 80%, recallpof about 60%, and FO.5of about 70% for predicting links between APls. Compared with the API network Necomposed of existing links on the current Web, Npcontains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recall, and discounted cumulative gain (DCG) on Npthan on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APls rather than using only existing links on the Web.