Semantic Web is freaking cool! (and on a roll it seems)

Been surfing around for semantic web websites to find ontologies or datasources to (ab)use for Yet Another AI-Project From Me. This is what I found:

• True Knowledge. Incredibly cool question-answering frontend to an incredibly complex datamodel, with a moderately complex and severely boring input process. Not free data, I can not download a dump of their database.


• Freebase. Took me some time to dig into this, actually, but I like what I am seeing. Data model seems a lot simpler than True Knowledge, or at least that is what i think (subclassing, transitivity missing?). Inputting stuff is from 2-10 times quicker/easier. For bulk stuff it is infinitely easier, since TK does not support that at all. Free data, but not RDF!


• Faviki. Very nice and easy to use semantic social tagging/bookmarking service.


• RDFScape. Visualizer for cytoscape. Very nice, have not had time to play with this yet.


• Attempto Controlled English. Maybe the least exciting of the bunch, but is useful for my NLP-related project.

I also got access to twine. Oh my god what a bore. I just did not see the idea behind it, and the interface turned me off so much that after my third visit I never came back.

True Knowledge has some awesome NLP parsing going on, but it also fails miserably often. I have a simpe idea to get me atleast started, it pretty much builds upon AIML/patterns to extract meaning from stuff, specifically Wikipedia.

Freebase has a weak model in my mind, there does not seem to be a real inheritance hierarchy and the "upper ontology" is basically missing. The upper ontology not being there is not such a big deal though, I think. There should be a set of "uppermost" classes in Freebase that can be mapped to SUMO/YAGO/DBPedia/Wordnet or whatever to help with any inferencing/analogous thinking.

I can not help but think that in five years from now, "semantic" does not really exist. Everything is then semantic, or gone since long. AGI is not far behind, either. I predict a surge of NLP success in the coming few years, mainly with knowledge-intensive approaches. Common-sense is still the missing piece of the puzzle, the above efforts do not concentrate on this at all, but rather on knowledge that is useful to humans. Remember, common-sense is boring for humans to input and administer, since it is all so basic.