Knowledge Representation and Information Retrieval: Research Papers as MoK Seeds

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In nowadays ICT systems, the amount of information to handle is usually huge, both in volume and in velocity of production/consumption.
This is especially true for those systems aiming at assisting users in creating knowledge out of information.
Academic social networks are one example of such a kind of systems, in which researchers consume information (scientific papers, datasets, etc.) to produce knowledge (insights, criticism, etc.) to be then reified and shared as novel information (a new paper).
In such a scenario, knowledge representation and information retrieval techniques assume a fundamental importance for enabling the computational system to deliver its functionalities, e.g., recommendations for related readings, clustering of similar papers, etc.

The aim of the thesis is thus that of investigating techniques for knowledge representation enabling (semantic) inference and discovery, as well as techniques for information retrieval in the field of academic publishing, with the goal of conceiving and developing a prototype implementation for MoK seeds and MoK injection reaction.