Similar to Google Sets functionality

Questions
1

I’m stunned to not find someone searching for this before.

Google Sets was a great labs project that allows you to get suggestions on the next word in a group. This was particularly useful if you forget the name of a website for example. It was deleted a year or 2 ago.

For example:
busuu, rosetta stone, livemocha, duolingo

and the suggestion might be “babbel”

There are keyword tools out there but this feature I think works better. I would have expected Wolfram to have this but I have tried many different syntax variations and still no result.

I hope I am posting in the right forum? Searching under functionality there appeared to be no category match… does this mean the feature simply isn’t there?

2

Google ended up incorporating it into some of its products such as it’s spreadsheet service: Google Sets, Still Available

The closest thing I can think of to replicating it in Mathematica is to use the Entity framework. So for example, If I went to find a list of things like “Harry Potter”, I would evaluate:

EntityTypeName@Interpreter["Entity"]["Harry Potter"]

This returns that Harry Potter is of type fictional character. We can ask for a list of example FictionalCharacters or we can maybe check to see which FictionalCharacter EntityClassLists contain him and return other Entities from the list.

3

Couldn’t Machine Learning be trained to do this? For example something similar in already built in training set - use the “FacebookTopic” built-in classifier to detect the topic of a Facebook post:

Classify["FacebookTopic", {"I bought a new computer", "happy birthday!", "this skirt looks nice"}]

{"Technology", "SpecialOccasions", "Fashion"}

You can come up with your own training sets, see examples here. Also I think could be done simpler via clustering. Say you can define relations between words. This is a toy example - not your case of course. Generate a network of “nearby” words in a dictionary:

words = DictionaryLookup["wol*"];
Flatten[Map[(Thread[# \[DirectedEdge] DeleteCases[Nearest[words, #, 3], #]]) &, words]];
CommunityGraphPlot@Graph[%, VertexLabels -> "Name", ImageSize -> 450]

wer34q5wterghsfbdvsc.png
wer34q5wterghsfbdvsc.png450×241 25.2 KB

Then determining to which cluster most of the words in your chain land, will result in getting another word from that cluster. I can imagine many other similar methods can be used.