On Tue, Aug 25, 2015 at 7:58 AM, Oliver Keyes <okeyes(a)wikimedia.org> wrote: Well, sure... but they describe an interesting search paradigm that I don't think we've even been considering (in the available paper). It's not the type of query-by-example I'm used to seeing. They intercept requests for wiki pages and convert infoboxes into structured query forms that allow some basic boolean syntax. It converts these queries into SPARQL and hits DBpedia to get results. Sounds reasonable. They do mention briefly in section 3.1 (last paragraph) that they basically need a custom ("page-dependent") mapping from any given infobox to appropriate internal representations for mapping to SPARQL. There are some obvious machine learning approaches to try there. Since they don't mention any machine learning, I assume they have done them manually, which may or may not scale, depending on how many queries of the sort they are interested in are covered by *n* manually mapped infobox types. Either way, it's potentially brittle, since the Wikipedians tending the infoboxes won't know about SWIPE. As for the comparison to Xser (which I'm not familiar with, though it's described here: http://ceur-ws.org/Vol-1180/CLEF2014wn-QA-XuEt2014.pdf ) and plain keyword searches in Wikipedia, I'd really need to see the full paper to comment properly, but I have some questions (which they may well answer in the paper). Plain keyword searches in Wikipedia are a fine baseline, though I wonder if they preprocessed the natural language queries, or just tossed the whole question into search (which it is not meant to handle, though it often works anyway). And I don't know what counts as success—one of the first *n* results contains the answer? How hard would a human have to look on a page for the answer? It seems that the SWIPE system requires a human to translate the query into the infobox template (and know which template to use!). So, for the query "who has Tom Cruise been married to?" (from the Xser paper), it seems the user has to convert "married to" into the "spouse(s)" field of the person infobox—which is pushing the NLP processing into the human (of which I am a fan, though it is not automatic). I'm not liking that they claim 96% recall "among all answered questions"—you don't get to ignore the ones you failed to answer when calculating recall! 100% precision is nice. Xser seems more like the NLP system I would have first imagined—parse a query, convert it into a structured format, and hit the RDF store for answers. SWIPE seems to get the human to do the hard parts (parsing and converting to a structured format, with the help of existing infoboxes), so of course it does better than Xser. So what do we get out of this? If you haven't already thought of WDQS, then you weren't paying attention! We could make things easier (for us, for SWIPE, for anyone), if we could develop a standard way to map infobox template fields to WDQS properties and contents to entities (someone must've thought of this already). Parsing the content of those fields (if you know what they are supposed to contain) is easier than parsing random queries or other chunks of text. That info could be used to automatically or semi-automatically populate WDQS, or to refer WDQS results back to relevant Wiki pages, or turn templates into query forms as SWIPE does. Whether any of this gets onto our roadmap this century is a different question, but there are some interesting things to think about here. So, can anyone get me a copy of the full paper? Thanks for the pointer, Tilman! —Trey Trey Jones Software Engineer, Discovery Wikimedia Foundation