用对话式交互+知识图谱，让学术搜索更智能。
Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs
- 通过对话理解用户意图，结合知识图谱实现语义搜索。
- 40人测试显示，对话界面比传统文本搜索更易发现相关论文。
- 适合科研人员快速探索文献，尤其擅长处理术语差异问题。
传统搜索依赖字符串匹配，而语义搜索通过识别查询词背后的意图和上下文含义，提升学术文献发现效果。由于用户查询与文档内容常存在词汇差异，传统方法易产生无关结果。许多学术搜索引擎已采用知识图谱表示作者、论文与研究概念之间的语义关系。然而，图形化界面数据复杂、信息量大，用户难以高效导航。为此，我们开发了一种基于知识图谱的对话式学术探索系统，详细阐述了其设计方法与架构。通过多种性能指标及40名参与者的主观评估，验证了该系统在发现相关文献方面的有效性，对比了对话界面与传统文本搜索及图形界面的表现，为对话式搜索系统的设计提供了实践指导。
原文摘要 · Abstract (English)
Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of search terms. Semantic search is particularly beneficial for discovering scholarly publications where differences in vocabulary between users' search terms and document content are common, often yielding irrelevant search results. Many scholarly search engines have adopted knowledge graphs to represent semantic relations between authors, publications, and research concepts. However, users may face challenges when navigating these graphical search interfaces due to the complexity and volume of data, which impedes their ability to discover publications effectively. To address this problem, we developed a conversational search system for exploring scholarly publications using a knowledge graph. We outline the methodical approach for designing and implementing the proposed system, detailing its architecture and functional components. To assess the system's effectiveness, we employed various performance metrics and conducted a human evaluation with 40 participants, demonstrating how the conversational interface compares against a graphical interface with traditional text search. The findings from our evaluation provide practical insights for advancing the design of conversational search systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。