I posted this on stackoverflow but want to get your recommendations as well as a user on overflow recommended I post it here.
I'm going to say from the beginning that I am not a programmer, I have a cursory knowledge of different types of AI and am just a businessman building a web app.
Anyways, the web app I am investing in to develop is for a hobby of mine. There are many part manufacturers, product manufacturers, upgrade and addon manufacturers etc. for hardware/products in this hobby's industry. Currently, I am in the process of building a crowd sourced platform for people who are knowledgeable to go in and mark up compatibility between those parts as its not always clear cut if they are for example:
Manufacturer A makes a "A" class product, and manufacturer B makes upgrade/part that generally goes with class "A" products, but is for one reason or another not compatible with Manufacturer A's particular "A" class product.
However, a good chunk (>60%-70%) of the products/parts in the database can have their compatibility inferred by their properties,
Part 1 is type "A" with "X" mm receiver and part 2 is also Type "A" with "X" mm interface and thus the two parts are compatible..
Part 1 is a 8mm gear, thus all bushings of 8mm from any manufacturer is compatible with part 1. Further more, all gears can only have compatibility relationships in the database with bushing and gear boxes, but there can be no meaningful compatibility between a gear and a rail, or receiver since those parts don't interface.
Now what I want is an AI to be able to learn from the decisions of the crowdsourced platform community and be able to infer compatibility for new parts/products based on their tagged attributes, what type of part they are etc.
What would be the best form of AI to tackle this? I was thinking a Expert System, but explicitly engineering all of the knowledge rules would be daunting because of the complex relations between literally tens of thousands of parts, hundreds of part types and many manufacturers.
Would a ANN (neural network) be ideal to learn from the many inputs/decisions of the crowdsource platform users?
Any help/input is much appreciated.