In recent years, several important content providers such as Amazon, Musicbrainz, IMDb, Geonames, Google, and Twitter, have chosen to export their data through Web services. To unleash the potential of these sources for new intelligent applications, the data has to be combined across different APIs.

To this end, we have developed ANGIE, a framework that maps the knowledge provided by Web services dynamically into a local knowledge base. ANGIE represents Web services as views with binding patterns over the schema of the knowledge base. In this talk, I will focus on two problems related to our framework.

In the first part, the focus will be on the automatic integration of new Web services. I will present a novel algorithm for inferring the
view definition of a given Web service in terms of the schema of the global knowledge base. The algorithm also generates a declarative script can transform the call results into results of the view. Our experiments on real Web services show the viability of our approach.

The second part will address the evaluation of conjunctive queries
under a budget of calls. Conjunctive queries may require an unbound number of calls in order to compute the maximal answers. However, Web services typically allow only a fixed number of calls per session. Therefore, we have to prioritize query evaluation plans. We are working on distinguishing among all plans that could return answers those plans that actually will. Finally, I will show an application for this new notion of plans.