AI

Adding Meaning to Facebook Microposts via a Mash-up API and Tracking Its Data Provenance

Abstract

The social networking website Facebook offers to its users a feature called “status updates” (or just “status”), which allows users to create microposts directed to all their contacts, or a subset thereof. Readers can respond to microposts, or in addition to that also click a “Like” button to show their appreciation 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). Therefore, we have implemented a RESTful mash-up NLP API, which is based on a combination of several third party NLP APIs in order to retrieve more accurate results in the sense of emergence. In consequence, our API uses third party APIs opaquely in the background in order to deliver its output. In this paper, we describe how one can keep track of provenance, and credit back the contributions of each single API to the combined result of all APIs. In addition to that, we show how the existence of provenance metadata can help understand the way a combined result is formed, and optimize the result combination process. Therefore, we use the HTTP Vocabulary in RDF and the Provenance Vocabulary. The main contribution of our work is a description of how provenance metadata can be automatically added to the output of mash-up APIs like the one presented here.