利用知识图谱联盟改进数字图书馆的分面搜索
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries
摘要：
科学家总是在文献中寻找最准确和最相关的答案。传统的学术数字图书馆在搜索结果中列出文档，因此无法为搜索查询提供精确的答案。换句话说，数字图书馆中的搜索是元数据搜索，如果可以的话，还有全文搜索。我们提出了一种方法，通过利用学术知识图谱的联邦来改进结构化内容的分面搜索系统。我们在学术知识图谱的基础上实现了该方法。该搜索系统可以利用来自第三方知识图谱的内容来改进学术内容的探索。我们的方法的一个新颖之处在于，我们在不同的数据类型上使用动态方面，这意味着方面可以根据用户查询而改变。用户还可以调整动态方面的粒度。另一个新颖之处是，我们利用第三方知识图谱来改进对学术知识的探索。
英文摘要：
Scientists always look for the most accurate and relevant answers to their queries in the literature. Traditional scholarly digital libraries list documents in search results, and therefore are unable to provide precise answers to search queries. In other words, search in digital libraries is metadata search and, if available, full-text search. We present a methodology for improving a faceted search system on structured content by leveraging a federation of scholarly knowledge graphs. We implemented the methodology on top of a scholarly knowledge graph. This search system can leverage content from third-party knowledge graphs to improve the exploration of scholarly content. A novelty of our approach is that we use dynamic facets on diverse data types, meaning that facets can change according to the user query. The user can also adjust the granularity of dynamic facets. An additional novelty is that we leverage third-party knowledge graphs to improve exploring scholarly knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
