Successfully using a software web-service/platform API requires satisfying its conceptual interoperability constraints that are stated within its shared documentation. However, manual and unguided analysis of text in API documents is a tedious and time consuming task. In this work, we present our empirical-based methodology of using machine learning techniques for automatically identifying conceptual interoperability constraints from natural language text. We also show some initial promising results of our research

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Search engines like Google play a critical role in life-long learning. However, the query capabilities of such engines remain simple and often yield a result set that is too large. In addition, search engines like Google rely on page ranking algorithm that represents the "collective consciousness" of millions of users. Learning about specifics often involves context. This paper shows how mind maps can be used as a contextual mechanism to specify what needs to be learned and to filter and retrieve the relevant sources of learning from the internet. In specific, a concept of "alignment" is introduced that filters only those information sources that are globally aligned to the mind map.The filter is implemented as a combinatorial optimization algorithm using Simulated Annealing.

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