Adding Meaning to Social Network Microposts via Multiple Named Entity Disambiguation APIs and Tracking Their Data Provenance

Abstract

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 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) and named entity disambiguation. Therefore, we have implemented a 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 to deliver its output. In this paper, we describe how one can keep track of data provenance and credit back the contributions of each single API to the joint result of the combined mash-up API. Therefore, we use the HTTP Vocabulary in RDF and the Provenance Vocabulary. In addition to that, we show how provenance metadata can help understand the way a combined result is formed, and optimize the result formation process.