Lexical/ontological/semantic knowledge base for physics 
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*Are there lexical/semantic knowledge bases for physics that can be used for automated reasoning and AI (like Princeton's Wordnet and MIT's Conceptnet for common sense English usage)?

*If not in existence, are there physics-specific issues to keep in mind while developing such a knowledge base? For example, can it be developed by using existing semantic relationship clauses in Conceptnet? Pointers to review papers, books etc will be good too.
I understand that such a knowledge base will not be complete without the mathematics related to the lexicon. But, when browsing through Q&As here, a lot of them do not seem to involve any mathematics at all (just pure English words). In any case, it should not be impossible to add the mathematics once there is a non-mathematical lexicon/semantic network (and there seem to be a few initiatives to include math in semantic web, like OpenMath, Content-MathML, OMDoc etc)
PS: This is not some crackpot rambling. I am a phd student in high energy theoretical physics. I arrived at this question while thinking about how much of current physics can be automated through AI.
edit 1: 
Something related: http://www.cs.utexas.edu/users/novak/physics.html
edit 2: Created a chat thread (update 2a: Apparently, the chat thread has been closed)
edit 3: Included few resources for included mathematics in semantic graph data
edit 4: Ed Shaya's astro-physics ontology, which also includes quite a bit of other areas of physics: http://www.astro.umd.edu/~eshaya/astro-onto/
 A: I'm the developer of a project called the Physics Derivation Graph, see it in GitHub, too.
My intention is to develop a set of derivations into a graph which would capture the current state of knowledge in Physics. Although I consider automated reasoning outside the scope of my project, you are welcome to look at the databases and think about what you can use.
I am intentionally avoiding dependence on English to construct the graph. The graph should be able to be analyzed by a computer algebra system. This implies it could be accessible to your interests in automated reasoning if you are approaching this mathematically.
PS: I too think I'm not a crackpot since I have a PhD in computational Physics

Edit 20150708: Link to the site and to the source code in GitHub.
A: I am the author of the astronomy and physics ontology mentioned in the original question.  The original purpose of that ontology was to improve search for data and articles in astronomy.  The idea was to have data sets tables and individual columns in tables marked up with relevant keywords. 
The rows of data in astronomy are usually different astronomical objects of different types.  A search could then be done for a range of values on a property for some type of object and the return would be all relevant data in all astronomical archives.  
But as we progressed, we thought of many more ways in which such an ontology could be used.  A newbie could quickly learn on his/her own from the ontology all the different astronomical species and subspecies, their properties and their brightest or closest examples.  One could ask for the latest papers dealing specifically on a particular type of observation of a particular type of object within a range of distances or direction on the sky.
I think most of this can carry over into physics as well.  One could ask for specific experiments or papers on a topic and then depending on the results ask for results on either broader or narrower terms.  Newbies can learn which terms mean nearly the same thing and how they differ, if they do differ.  
One thing we looked at is whether a complex long paper can be boiled down to a few simple ontological statements.  It helps that a reasoning machine can tell you which statements are repeats of already known things and which are new.  Then, with training one could read the results of an entire Physics Review journal in a few minutes. The list goes on and on.
However, the funding required to do this is large and right now the only groups that I see doing these kinds of things are Microsoft, Google and Apple, and all of that is behind closed doors.
A: You might find this paper interesting:

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics. Mario Krenn and Anton Zeilinger. arXiv:1906.06843 (2019).

In their own words,

Here we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet

where they use Wikipedia as a source of concepts forming nodes in the knowledge network, and the published literature as a source for edges linking those nodes together with the strength of those edges.
I don't know how useful this will end up being, but it's worth a look if you're interested in that genre.
