Google is letting spam through, just like they did with link spam. The logic is that by allowing this spam short term, they can collect the data and use that to train the algorithms to understand what Knowledge Spam looks like. This allows Google to train the algorithms to recognise the spam people are using today, but also recognise (and exclude) future as-yet-unthought-of spam techniques and resources.
Making a comparison with Penguin: originally, Penguin consisted of multiple manual updates by Google engineers. These manual updates were steps in a process to build a self-learning anti-spam algorithm and integrate it into the core algorithm. That’s huge – the anti-spam Penguin algorithm is now part of the core algorithm and it can identify spam links and algorithmically exclude them. Importantly, it can identify spam link types it already knows (from those manual updates), but it is additionally semi-autonomously learning to identify and algorithmically exclude new techniques spammers are implementing today and in the future.
In short: Google engineers train the machine to understand what spam looks like instead of manually excluding it from the algorithm. So if they allow humans to create spam and trick the machine, they will find the spam based on the data feedback, and send the data back as corrective data. Gradually, the machine will learn “what spam looks like” and be able to dynamically exclude spam it has never seen before.
Google has perfected this sequence for letting spam through, collecting the data and building an ML algorithm to combat the spam. They did this with Penguin and Panda. They will now do it with Knowledge.
Two important things here: because the engineers have the experience of building Penguin and Panda, they will be able to develop this new anti-knowledge-spam algorithm faster, and also since Machine Learning technology is improving exponentially, the machine will learn faster than ever before. Don’t expect Knowledge Panel Spam to be the huge, long term problem that link spam represented.
Analogy: Teaching a Child
Machine Learning Spam Algorithms can be compared to teaching a child. If you encourage the child to go to the library and research and learn for themself, they will be learning to learn. If you tell that same child everything they need to know, they will never learn to learn. It will always come to you and ask for the answer. The engineers are teaching the algorithms to learn to learn.