Hi, yesterday I wanted to raise a possibility of use case scoping for practical example, based on WHY and HOW TO questions rather then factual questions (WHERE; WHEN; WHAT) and wanted to hear your opinion. I created a use case with the template here: https://www.notion.so/Use-Case-scoping-for-a-practical-goal-Example-c7df043346954555a8a3333935b217c0 I included an introduction to explain what I am thinking about.
Hi Luigi A., now I am even more sorry we did not get to your question yesterday. This is very interesting! :) I like the idea that womeone might come form a practical point of view as you describe it. We should definitly intergate it in the model in some form. Maybe as a Topic "Practical aplication" ? We then need to think of how grannular we might want to go. Is the goal to orient the person to specific content or give a straight answer.
Imagine this. You have knowledge about nlp. You are interested in climate change. How d you get to apply nlp for cc ? In my opinion (my strategy would be) go backward. So cc may unpack in climate mitigation, sequestration etc. So an example may be structuring the onthologies of cc into kg, nlp may map the steps into a general human spoken question. I d find useful to see all the steps unpacked in a learning path (that s why i built my platforms). How granular are the steps ? In my opinion a step should be clear enough for a layperson, for generic use. I d also keep the logic distinct: why and how to navigate insights, other factual info( where what hetherogeneous data video media podcast etc) aggregated on top. It is important, in my opinion, what a user can do ... Wrapping up, i ll start from a domain (climate change, ethology and taxonomy, food discovery, bibliographic search....) and backward. Tech concepts are the last part and just functional to make the life of a passionated human being easier... Lol
There are a lot of different things in what you are describing. We might not want to tackle all of them at the beginning. But I like the idea that someone might come from a specific domain and wants to learn about KG from his domain point of view, and find out how KG could be applied in it. My takeaways from your use case are that we should in some way model the concepts of "an application of KG" and "a (industry?) domain". I am not sure we want to model the "learning path steps" though. I would say that this information would be infered from the KG content.
Oh yeah inference of learning paths is big time. How would you deal with it matthias?
That is an excellent question 馃槣 My hope is that the KG would model Topics, Person, their domain of work (background), the Question they asked , and the Answers. From their I would try to define persona by clustering persons based on their domain of work. Then I would look at the questions they asked and try to extract the topics mentionned in the answers. It is simply an idea from the top of my head, but that is probably how I would start 馃槈
I love this idea.
If it would be possible to develop a learning path for KGs using a KG that would be excessively satisfying. A bit like a 3D printer printing itself.
I worked on multipartite graphs and I think that approach can be valid : recommender systems can be approximated to bipartite graphs (users, content), I aimed to to generalise recommendations between different types of entities, by leveraging on entities properties. As results, recommendations are good enough even if where there are no followers (or too few users). I would use graph analytics on top of KG, where KG may describe types of Q and A. Elaborating on your hint Matthias S. - Grouping of Personae could be done in different ways, right now thinking about similarity measures but in case of dense networks also dimensionality reduction may proof good to cluterize. I would groups types of questions and answers ( maybe bread for NLP teeth ), and ascribe those to types of "meta"-relationships in a KG, in attempt to abstract the scope of questions / answers from granularity of topics (like, imagine to map "raw ingredients" and "cooking details" into commodity classification and cooking procedures). And then use relationships and extracted topics as properties in topological analyses, to classify types of Persons. Inference could be done upon these results. I used random walks between entities; I think it could be done between subgraphs of persons-topics-persons. I tried random walks because reasoning does not always work well with shortest paths answers - but rather organising paths that are not trivial, and also not too long. They proved insightful. I also would like to explore new approaches. So to wrap up, I would use a KG to describe types of entities and relationships, and then use network science analytics to organise topics, persons, background, and then use statistical inference for suggestions of intermediate steps of learning paths. For the sake of knowledge - any public grant to apply for a #common work ? Or Robert M. does companies like McKinsey and similar may offer a small grant or would be receptive of a pilot proposal (cost-benefit opportunity 馃檪 ) ?
Here an example from my experiments - tell me a pathway between the concept of "life" and "artificial intelligence" ? These results are only obtained from a simplified knowledge network that does not know about types of entities: topics, people, place, etc. are all treated the same way. And yet, you see the reasoning is not trivial. I think adding a KG to guide the "reasoning" through types of topics and to unpack generic concepts (like "computer science") into backgrounds of people would really be ... excessively satisfying 馃檪 (Apologise if I elaborated outside of the scope of bookclubontology)
Hi Luigi A. This project (the book club ontology) is something I am interested in in a personal capacity: it鈥檚 not directly of relevance to my work at McKinsey, so I鈥檓 afraid there鈥檚 not likely to be financial support for this. Having said that, one of my goals is to bring the firm rapidly into this space in advance of what I see as the inevitable migration of industry. So in the medium to long term, there may well be opportunities for us to leverage external consultants and developers. In that case, though, critical will be tying the project directly and feasibly to real business need, which is no mean feat.
Ok!
