{"id":241604,"date":"2016-06-23T16:35:31","date_gmt":"2016-06-23T23:35:31","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=241604"},"modified":"2018-10-16T22:07:49","modified_gmt":"2018-10-17T05:07:49","slug":"241604","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/241604\/","title":{"rendered":"The rise of the machines: Artificial intelligence meets scholarly content"},"content":{"rendered":"
Key<\/span> points<\/span><\/strong><\/div>\n
    \n
  • New<\/span> forms<\/span> of<\/span> human\/machine<\/span> dialogue<\/span> are<\/span> emerging<\/span> as<\/span> robots<\/span> understand<\/span> vast <\/span><\/span>amounts<\/span> of<\/span> content<\/span> rather<\/span> than<\/span> simply<\/span> indexing<\/span> content<\/span> as<\/span> strings<\/span> of<\/span> characters.<\/span><\/li>\n
  • Recognizing<\/span> strings<\/span> of<\/span> charact<\/span>ers<\/span> as<\/span> entities<\/span> (e.g.<\/span> =<\/span> names<\/span> =<\/span> authors)<\/span> allows<\/span> for<\/span> mean<\/span><\/span>ingful<\/span> associations<\/span> between<\/span> entities<\/span> and<\/span> reasoning<\/span> over<\/span> these<\/span> relationships.<\/span><\/li>\n
  • Web-scale<\/span> adoption<\/span> of<\/span> the<\/span> Semantic<\/span> Web<\/span> approach<\/span> has<\/span> been<\/span> slow<\/span> because<\/span> it<\/span> is<\/span> too <\/span><\/span>complex<\/span> to<\/span> implement<\/span> and<\/span> does<\/span> not<\/span> scale.<\/span><\/li>\n
  • User<\/span> intent,<\/span> discovered<\/span> through<\/span> conversat<\/span>ional<\/span> models<\/span> of<\/span> human<\/span>\u2013<\/span>computer<\/span> interac<\/span><\/span>tion,<\/span> allows<\/span> for<\/span> a<\/span> deeper<\/span> understanding<\/span> of<\/span> exactly<\/span> what<\/span> researchers<\/span> are<\/span> looking<\/span> for.<\/span><\/li>\n
  • Personal<\/span> agents<\/span> hold<\/span> the<\/span> promise<\/span> of<\/span> \ufb01<\/span>nding<\/span> information<\/span> that<\/span> we<\/span> will<\/span> \ufb01<\/span>nd<\/span> useful<\/span> before <\/span><\/span>we<\/span> have<\/span> started<\/span> to<\/span> look<\/span> for<\/span> it.<\/span><\/li>\n
  • Publishers<\/span> can<\/span> use<\/span> Academ<\/span>ic<\/span> Knowledge<\/span> APIs<\/span> to<\/span> interpret<\/span> academic<\/span> user<\/span> queries<\/span> and <\/span><\/span>\ufb01<\/span>nd<\/span> rich<\/span> information<\/span> from<\/span> the<\/span> Microsoft<\/span> Academic<\/span> Graph<\/span><\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    Key points New forms of human\/machine dialogue are emerging as robots understand vast amounts of content rather than simply indexing content as strings of characters. Recognizing strings of characters as entities (e.g. = names = authors) allows for meaningful associations between entities and reasoning over these relationships. 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