Disclaimer: didn't read the whole thing, but here's some feedback:
> Graphs are not only useful as cognitive aides, but are suitable data structures for a wide variety of tasks, particularly on modern parallel processing hardware.
I hope you mean by parallel processing, on a single machine. Reasoning on distributed graphs are difficult to do. The semantic we community could talk for days about what doesn't work.
> Sets: datasets, multisets, posets, alphabets
I guess by alphabets you mean dictionaries.
When you talk about types as graphs I guess you mean nested Objects with collection attributes/types
Where I agree with you is that graphs feel more natural than other structures because our brain is a gigantic graph of neurons.
I don't agree that the web is a directed graph. Not even the semantic web because of a lack of data quality, ridiculous ontologies, and a massive amount of parrtaly duplicating the same instances.
I wouldn't include formula or code parsing because in such cases they are just a means to an end.
What I would include on the other hand are execution plans of data bases which are used for query optimization. You might want to talk here to someone at Oracle.
That all being said, I'd like to reiterate on talking to the semantic web community. Maybe start networking at the ISWC conference first. I always dreamt of every ody's personal knowledge graph where explicit data is linked through everybody's individual ontology into something that is particularly understandable for the creator of the specific ontology.
They definitely need someone with a different perspective. The vuys from Cambridge Semantics are quite good.
> I hope you mean by parallel processing, on a single machine.
Not necessarily! Parallelism in the more general sense. We can parallelize matrix multiplication and there is good research on distributing graph algorithms across multiple machines. Spielman discusses this in his recent talk on Algebraic Graph Theory:
Just unordered collections of symbols. Depending on the dictionary or alphabet implementation, it might have an ordering.
> I don't agree that the web is a directed graph.
This was intentionally vague, but depending on which types of links you consider it may or may not have directionality. Hyperlinks, for example, are directional.
> I wouldn't include formula or code parsing because in such cases they are just a means to an end.
Part of my goal is to show how simple implementing these algorithms can be. Agree it doesn't flow very well, maybe it should be in the appendix.
> You might want to talk here to someone at Oracle.
Agreed, I think the work they're doing in probabilistic programming languages is super interesting. I recently gave a short presentation about loopy belief propagation (another interesting graph algorithm for PGMs).
> That all being said, I'd like to reiterate on talking to the semantic web community...Cambridge Semantics are quite good
Will definitely look into them, thank you! I am fascinated by the whole topic of knowledge bases and semantic parsing, and appreciate the suggestions you provided.
> Graphs are not only useful as cognitive aides, but are suitable data structures for a wide variety of tasks, particularly on modern parallel processing hardware.
I hope you mean by parallel processing, on a single machine. Reasoning on distributed graphs are difficult to do. The semantic we community could talk for days about what doesn't work.
> Sets: datasets, multisets, posets, alphabets
I guess by alphabets you mean dictionaries.
When you talk about types as graphs I guess you mean nested Objects with collection attributes/types
Where I agree with you is that graphs feel more natural than other structures because our brain is a gigantic graph of neurons.
I don't agree that the web is a directed graph. Not even the semantic web because of a lack of data quality, ridiculous ontologies, and a massive amount of parrtaly duplicating the same instances.
I wouldn't include formula or code parsing because in such cases they are just a means to an end.
What I would include on the other hand are execution plans of data bases which are used for query optimization. You might want to talk here to someone at Oracle.
That all being said, I'd like to reiterate on talking to the semantic web community. Maybe start networking at the ISWC conference first. I always dreamt of every ody's personal knowledge graph where explicit data is linked through everybody's individual ontology into something that is particularly understandable for the creator of the specific ontology.
They definitely need someone with a different perspective. The vuys from Cambridge Semantics are quite good.