Machine Learning - Special issue on learning with probabilistic representations
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
Mining Software Repositories with iSPAROL and a Software Evolution Ontology
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
sMash: semantic-based mashup navigation for data API network
Proceedings of the 18th international conference on World wide web
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With a mashup network in which a link indicates that two applications are mashupable, building a mashup can be simplified into network navigation. This paper presents an approach that constructs a Web mashup network by learning a semantic Bayesian network using a semi-supervised learning method. An RDF model is defined to describe attributes and activities of applications. To process all information sources on the Semantic Web, a semantic Bayesian network (sBN) is proposed where a semantic sub graph template defined using a SPARQL query is used to describe the information about the graph structure. The sBN offers more powerful abilities to process the information sources on Semantic Web, especially the graph structure. To improve the learning performance, a semi-supervised learning method that makes use of both labeled and unlabeled data is proposed. We ran the approach on a data set containing 100 applications collected from the website Programmableweb.com and 3077 links checked manually. The results show that the approach outperforms the PRL and the rule-based methods, and the semi-supervised learning method achieved big improvements in recall and, compared with the direct learning method.