Companies have a massive amount of information available within reach, only a few clicks away. The challenge with these large volumes of information is that we, as a human being, are not able to digest it properly. Search Engines like Google are helping to prioritise information based on your query and interests. However, we noticed, with the trend of open data, that not all data is indexed in Google, leaving multiple sources “undiscoverable”.

One type of information is the local, regional and federal political information. Vast amounts of reports, detailed research documents, … are available for mining but it is difficult to valorize them, since the information is “stuck” in PDF, or, as mentioned, is not indexed by Google. Many political decision documents have become more and more open-source. Albeit on a local municipal level or on a district level, these data and metadata have found their way into Linked Open Data platforms and databases.

In this project, we wish to solve this challenge. We want to enable many companies to reach that information with ease, based on their interests in the political chatter available about their company, line of work or sector.

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A traditional approach to this problem is to have literal people spending hours combing through government statements and meeting notes to sift out nuggets of information pertaining to a certain context. A more modern approach however, is to use advanced NLP techniques to do this automatically on a large scale.