Towards better understanding of academic search
Towards better understanding of academic search
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DOI：
10.1145/2910896.2910922
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Madian Khabsa;Zhaohui Wu;C. Lee Giles
中科院分区：
文献类型：
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作者：
Madian Khabsa;Zhaohui Wu;C. Lee Giles
学术界在很大程度上依赖搜索引擎来识别和定位与其研究领域相关的研究手稿。许多早期的信息检索系统和技术是在为图书馆员提供服务的同时开发的，以帮助他们筛选书籍和会议记录，紧随其后的是最近的在线学术搜索引擎，如谷歌学者和微软学术搜索。尽管学术搜索引擎在学术界很受欢迎，而且对学术界具有重要意义，但学术搜索引擎的使用、查询行为和检索模型还没有得到很好的研究。为此，我们研究了学术搜索引擎接收到的查询的分布。此外，我们更深入地研究学术搜索查询，并将其分类为导航查询和信息查询。这项工作引入了学术搜索引擎中导航查询的定义，根据该定义，如果用户正在搜索特定的论文或文档，则查询被认为是导航的。我们描述了导航学术查询的多个方面，并引入了一种带有一组特征的机器学习方法来识别此类查询。
Academics have relied heavily on search engines to identify and locate research manuscripts that are related to their research areas. Many of the early information retrieval systems and technologies were developed while catering for librarians to help them sift through books and proceedings, followed by recent online academic search engines such as Google Scholar and Microsoft Academic Search. In spite of their popularity among academics and importance to academia, the usage, query behaviors, and retrieval models for academic search engines have not been well studied. To this end, we study the distribution of queries that are received by an academic search engine. Furthermore, we delve deeper into academic search queries and classify them into navigational and informational queries. This work introduces a definition for navigational queries in academic search engines under which a query is considered navigational if the user is searching for a specific paper or document. We describe multiple facets of navigational academic queries, and introduce a machine learning approach with a set of features to identify such queries.