基于深度学习的学术搜索引擎——Semantic Scholar
发布时间：2019-03-17 12:20
【摘要】：[目的/意义]Alpha Go战胜李世石后,人工智能的研究与发展备受关注。在此之前不久,基于深度学习的Semantic Scholar免费学术搜索引擎的问世,也为科研工作者们搜索和筛选学术文献资源带来了新的体验。[方法 /过程]在介绍人工智能、机器学习和深度学习之间关系的基础上,介绍了Semantic Scholar的检索功能,重点就该引擎基于系统在理解文献内容基础上的学术影响力评价功能作了分析,并将Semantic Scholar与现行主流学术搜索引擎Google Scholar、Microsoft Academic、必应学术和百度学术进行比较研究。[结果/结论]Semantic Scholar通过机器学习可以使系统理解不同引用之间的影响力差异,提出了基于引用内容分析的学术影响力评价指标,但在信息来源、学科范围、检索功能和个性化服务功能方面还有待进一步完善。最后提出今后学术搜索引擎的发展展望。
[Abstract]:Objective: after Alpha Go defeated Li Shi-Shi, the research and development of artificial intelligence (AI) has been paid more and more attention. Not long ago, Semantic Scholar, a free academic search engine based on in-depth learning, brought new experiences to researchers in searching and screening academic literature resources. [methods / processes] on the basis of introducing the relationship among artificial intelligence, machine learning and in-depth learning, this paper introduces the retrieval function of Semantic Scholar. This paper focuses on the analysis of the academic influence evaluation function of the engine based on the understanding of the contents of the literature, and compares Semantic Scholar with the current mainstream academic search engine Google Scholar,Microsoft Academic, Bing academic and Baidu academic research. [results / conclusion] through machine learning, Semantic Scholar can make the system understand the difference of influence among different citations, and put forward the evaluation index of academic influence based on citation content analysis, but in the information source and subject scope, The retrieval function and personalized service function need to be further improved. Finally, the development prospect of academic search engine in the future is put forward.
【作者单位】： 北京化工大学图书馆;
【分类号】：G252.7;G434
，
本文编号：2442296
[Abstract]:Objective: after Alpha Go defeated Li Shi-Shi, the research and development of artificial intelligence (AI) has been paid more and more attention. Not long ago, Semantic Scholar, a free academic search engine based on in-depth learning, brought new experiences to researchers in searching and screening academic literature resources. [methods / processes] on the basis of introducing the relationship among artificial intelligence, machine learning and in-depth learning, this paper introduces the retrieval function of Semantic Scholar. This paper focuses on the analysis of the academic influence evaluation function of the engine based on the understanding of the contents of the literature, and compares Semantic Scholar with the current mainstream academic search engine Google Scholar,Microsoft Academic, Bing academic and Baidu academic research. [results / conclusion] through machine learning, Semantic Scholar can make the system understand the difference of influence among different citations, and put forward the evaluation index of academic influence based on citation content analysis, but in the information source and subject scope, The retrieval function and personalized service function need to be further improved. Finally, the development prospect of academic search engine in the future is put forward.
【作者单位】： 北京化工大学图书馆;
【分类号】：G252.7;G434
，
本文编号：2442296
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