期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2013
卷号：5
页码：69-78
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Social networking sites such as Facebook or Twitter let their users create microposts directed to all, or a subset of their contacts. Users can respond to microposts, or in addition to that, also click a Like or ReTweet button to show their appre- ciation for a certain micropost. Adding semantic meaning in the sense of unambiguous intended ideas to such microposts can, for example, be achieved via Natural Language Processing (NLP) and named entity disambiguation. Therefore, we have imple- mented a mash-up NLP API, which is based on a combination of several third party NLP APIs in order to retrieve more accu- rate results in the sense of emergence. In consequence, our API uses third party APIs opaquely in the background to deliver its output. In this paper, we describe how one can keep track of data provenance and credit back the contributions of each sin- gle API to the joint result of the combined mash-up API. There- fore, we use the HTTP Vocabulary in RDF and the Provenance Vocabulary. In addition to that, we show how provenance meta- data can help understand the way a combined result is formed, and optimize the result formation process.
关键词：social networks; data provenance; web services; named ; entity disambiguation; named entity consolidation
