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Semantic Scholar 创建于 2015 年，是一个致力于解决信息超载的学术文献搜索引擎。它利用人工智能（AI）技术帮助用户从浩如烟海的文献中筛选有用信息，且能在一定程度上理解这些内容。这个免费工具由华盛顿州西雅图的艾伦人工智能研究所（Allen Institute for Artificial Intelligence）研发推出。值得一提的是，这家机构是在 2014 年由微软创始人之一保罗·艾伦（Paul Allen）参与创立的。 Semantic Scholar 的主页面：www.semanticscholar.org （不需翻墙） 最初 Semantic Scholar 仅关注计算机科学，但在去年增加了神经科学的文献；而今它又将其关注领域扩展了到了生物医学，涵盖了数百万计收录于 PubMed 等索引平台的研究文献。目前使用者能够通过 Semantic Scholar 获取近 4000 万篇文献。2016 年，Semantic Scholar 的程序增加了评价科研人员及研究机构的功能。这个评价体系基于引用处上下文内容、排除自引等规则来定义论文的“高影响力引用”。同年 4 月，Semantic Scholar 就曾给出过计算机科学领域内的研究者排名——最具影响力的计算机科学家是在圈外鲜为人知的人工智能先驱、加州大学伯克利分校的迈克尔·乔丹。“大家都叫他机器学习领域的迈克尔·乔丹，”西雅图艾伦人工智能研究所的负责人，Semantic Scholar 的创造者奥伦 · 埃齐奥尼（Oren Etzioni）如此打趣。 当 2016 年 11 月它的数据库扩展到神经科学领域后，Semantic Scholar 也被用来评价脑科学和神经科学研究者的影响力；现在，它推出了生物医学领域学者影响力的排名。 除了 SemanticScholar，现今已有的很多其他学术搜索引擎，比如谷歌学术（Google Scholar）和微软学术搜索（Microsoft Academic Search），也都能够帮助特定领域的专业研究人员查找到他们想要的信息。但海格曼表示，如果想要探索不同领域间的联系，或者一些新的领域，那么没有任何一个工具能够像 Semantic Scholar 一样提供“发现式体验”。 Semantic Scholar能够生成科学家之间的学术交流关系，这只是它众多功能中的一条。 宾夕法尼亚大学的一位数据科学家丹尼尔·希梅尔斯坦（Daniel Himmelstein）认为：“通用型的搜索引擎可能会变得更加先进，因此我们可能不再需要专门的学术搜索引擎。”他说，“（学术搜索引擎）很难打败已经经过几十年训练、从整个互联网中检索信息的搜索引擎。 小编知道如此神奇后急不可耐的搜了一搜，比如业界的院士大牛，如施一公，试了试，发现学术合作伙伴立显! 施一公: 大家看看，有没有颜宁呢？显而易见，右边有Nieng Yan，我们继续点击Nineng Yan ，也能找到两者关系！当然其他人也在列！ 颜宁: 当然还有免费的PDF下载，这里要提一下，前几天Research gate还遭到爱思唯尔杂志期刊的起诉，看来起诉没用的，这里又冒出这个available pdf。看来，期刊pdf开源是趋势啊！ 原文报道： Who’s the most influential biomedical scientist? Computer program guided by artificial intelligence says it knows PMEric Lander, president and founding director of the Broad Institute and a biologist at the Massachusetts Institute of Technology in Cambridge, is the most influential biomedical researcher of the modern era, according to a computer program. Lander, a geneticist and mathematician, ranks first on a new list of top biomedical researchers produced by the scientific literature search tool Semantic Scholar. Semantic Scholar, launched in 2015, is an academic search engine aiming to tackle the problem of information overload. It uses artificial intelligence (AI) to help users sift through huge numbers of scientific papers and understand (to a limited extent) their content. The free tool was developed by the Allen Institute for Artificial Intelligence (AI2), a nonprofit based in Seattle, Washington, that was co-founded in 2014 by Microsoft Co-Founder Paul Allen. Semantic Scholar’s archive of searchable literature initially focused on computer science, and last year expanded to include neuroscience. Today, it is expanding again, to include the millions of biomedical research papers indexed by PubMed and other sources; overall, Semantic Scholar’s archive is now approaching 40 million papers. By signing up, you agree to share your email address with the publication. Information provided here is subject to Science's Privacy Policy. Last year, Semantic Scholar’s programmers also added functionality that allows it to measure the influence of researchers and organizations, based on what they call "highly influential citations"—which takes into account the context around citations, excluding any self-citations—and other information. In April 2016, the tool ranked computer scientists, and when its corpus was expanded to neuroscience in November 2016, it was also used to judge the most influential brain scientists. Now, Semantic Scholar is ranking biomedical researchers. Here’s the list of the top 10, provided to ScienceInsider: 1 Eric Lander, Massachusetts Institute of Technology (biology) 2 Karl Friston, University College London (neuroscience) 3 Raymond Dolan, University College London (neuroscience) 4 Shizuo Akira, Osaka University (immunology) 5 David Botstein, Calico (biology) 6 Dennis Smith, Pfizer (pharmacokinetics) 7 Eugene Koonin, National Center for Biotechnology Information (biology) 8 Walter Willett, Harvard School of Public Health (epidemiology) 9 Rudolf Jaenisch, Massachusetts Institute of Technology (genetics) 10 Bert Vogelstein, Johns Hopkins Medical School (oncology) (Friston and Dolan, neuroscientists who hold the second and third spots on the list, respectively, also held the top two positions on Semantic Scholar’s list of most influential neuroscientists.)The absence of women on the list has drawn attention on social media, with some researchers wondering if the result reflected a bias in Semantic Scholar’s ranking algorithm, or is another expression of long-documented differences in gender representation in the biomedical sciences and scientific publishing. In a statement, AI2’s Marie Hagman, a senior product manager who oversees Semantic Scholar, said: "I think the fact that there are no women in the Top 10 authors by the highly influential citation analysis done by AI2 is spotlighting the well-reported problem of publication bias in science and in the context of the current global conversation on gender. It's encouraging to see that people are paying more attention to this issue, as the all-male list last year didn't receive this kind of buzz."Information overload With scientific literature doubling roughly every 9 years, keeping up is becoming increasingly difficult, Hagman says. There’s "a ton of information trapped in these articles and we want to bring it to life," she says. "We think there are potential cures or ways to improve or save human lives that may be buried away in a PDF somewhere."Semantic Scholar gets used on average a million times each month, Hagman says. Ultimately, she hopes that the tool can go even further in the content it extracts, perhaps by even suggesting hypotheses for researchers to test. And she envisions the tool pulling data and comparing similar experiments from different papers. "An automated meta-analysis is certainly something we believe is on the horizon," Hagman says. One limitation of the tool is that it can’t trawl paywalled papers. Hagman notes, however, that her group is negotiating with publishers for varying levels of access. Many other academic search engines, such as Google Scholar and Microsoft Academic Search, already exist. And any of these search tools will do the job for those who are experts in a particular field and know what they are looking for, Hagman says. But for those exploring connections between different fields or looking into new areas, she believes no other tool provides the "discovery experience" offered by Semantic Scholar. Randy Olson, an AI researcher at the University of Pennsylvania (UPenn), says Semantic Scholar is "far more useful" than Google Scholar. "Could Semantic Scholar’s AI piece together that a relatively unimportant discovery in one field is a groundbreaking solution to a major challenge in another field?" he asks. "Only time will tell, but I’m optimistic."But in the future, "general purpose search engines may become so advanced that there’s no need for academic engines," notes Daniel Himmelstein, a data scientist at UPenn. "It’s going to be hard to beat search engines trained on decades of searches across the entire web at information retrieval." 如果您觉得这篇文章不错，请转发到您的朋友圈吧！ |
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