  Understanding and analyzing big data is firmly recognized as a powerful and
strategic priority. For deeper interpretation of and better intelligence with
big data, it is important to transform raw data (unstructured, semi-structured
and structured data sources, e.g., text, video, image data sets) into curated
data: contextualized data and knowledge that is maintained and made available
for use by end-users and applications. In particular, data curation acts as the
glue between raw data and analytics, providing an abstraction layer that
relieves users from time consuming, tedious and error prone curation tasks. In
this context, the data curation process becomes a vital analytics asset for
increasing added value and insights.
  In this paper, we identify and implement a set of curation APIs and make them
available (on GitHub) to researchers and developers to assist them transforming
their raw data into curated data. The curation APIs enable developers to easily
add features - such as extracting keyword, part of speech, and named entities
such as Persons, Locations, Organizations, Companies, Products, Diseases,
Drugs, etc.; providing synonyms and stems for extracted information items
leveraging lexical knowledge bases for the English language such as WordNet;
linking extracted entities to external knowledge bases such as Google Knowledge
Graph and Wikidata; discovering similarity among the extracted information
items, such as calculating similarity between string, number, date and time
data; classifying, sorting and categorizing data into various types, forms or
any other distinct class; and indexing structured and unstructured data - into
their applications.
