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Domain: dbpedia.org

Stories and comments across the archive that link to dbpedia.org.

Comments · 6

  1. Could start with Jasper (was: Re:Free alternative) by erikscott · · Score: 1 · on Microsoft Bringing Cortana To iOS, Android

    You could, presumably, start with Jasper... graft on a bunch of Python for DBpedia, and then a heads-up interface for your powered exoskeleton.

  2. A semantically-based solution based on Wikipedia by trendspotter · · Score: 1 · on Ask Slashdot: Tags and Tagging, What Is the Best Way Forward?

    Here is a solution: DBpedia - A community effort to extract structured information from Wikipedia resulting in a semantically-based solution (ontology) http://dbpedia.org/About http://en.wikipedia.org/wiki/DBpedia

  3. Re:URL representing a given subject by zmughal · · Score: 1 · on Wikipedia As a "War Zone," Rather Than a Collaboration

    Wikipedia is good for use as a URL representing a given real-world subject. For example, an article about graphics in Linux could refer to "this DRM, not that other DRM"

    Which is why DBpedia (which is based on Wikipedia) plays such a central role for Linked Data.

  4. Re:Not "big data" by gajop · · Score: 1 · on Researcher's Wikipedia Big Data Project Shows Globalization Rate

    there are some common dumps, like http://wiki.dbpedia.org/Downloads37

  5. Re:Where All... by moustaki · · Score: 1 · on Six Degrees of Wikipedia

    Try DBpedia. You can query the infoboxes through SPARQL.

  6. Re:We still have no clue how to do strong AI by Anonymous Coward · · Score: 0 · on Smarter-than-Human Intelligence & The Singularity Summit

    AI looked so close in the 1960s, once it was realized that you could get a computer to do mathematical logic. All that was necessary was to express the real world in predicate calculus and prove theorems. After all, that's how logicians and philosophers all the way back to Aristotle said thinking worked. Well, no. We understand now that setting up the problem in a formal way is the hard part. That's the part that takes intelligence. Crunching out a solution by theorem proving is easily mechanized, but not too helpful. That formalism is too brittle, because it deals in absolutes.

    Theorem proving is increasingly usable for software verification ( http://research.microsoft.com/specsharp/, http://research.microsoft.com/projects/z3/, http://ase.arc.nasa.gov/projects/certifiableSyn/, etc.), and mathematics ( http://www.math.pitt.edu/~thales/flyspeck/, http://en.wikipedia.org/wiki/Robbins_algebra, http://en.wikipedia.org/wiki/Four_color_theorem, http://mmlquery.mizar.org/mmlquery/fillin.php?fill edfilename=mml-facts.mqt&argument=number+102, http://ea.unicyb.kiev.ua/sad.en.html). So much for Aristotle (and Leibniz, Babbage, Turing, von Neumann, ...): thinking often works. Brittleness is a problem in mathematics too (most of math is not stated formally), but bigger problem is that theorem proving is far from "easy" (undecidable generally). There has been progress in all of this: methods of dealing with ambiguity, more and more knowledge becoming less ambiguous and available for formal reasoning (semantic web and other annotations - sometimes automatic, http://dbpedia.org/), methods of formal reasoning becoming smarter and combined with other AI approaches. Pessimism based on heuristic pseudocounterarguments is an easy option, but it has not helped much in recent solving of hard "impossible" problems like Fermat's Last Theorem, Poincare conjecture, Four Color Theorem, neither did pessimists invent computers, and eventually beat humans in chess with them.