用AI+知识图谱帮学者快速找文献，自然语言提问直接出答案。
Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach
- 融合向量搜索、大模型与知识图谱，实现自然语言文献检索。
- 用户提问后自动提取信息并生成答案，提升查找效率。
- 适合科研人员快速定位相关论文，尤其擅长复杂问题探索。
随着学术文献数量持续增长，寻找相关文献变得愈发困难。生成式人工智能（尤其是大语言模型）的兴起为文献发现与探索带来了新可能。我们提出ASK（Scientific Knowledge Assistant），一个基于神经符号方法的AI驱动学术文献搜索与探索系统。该系统通过向量搜索、大语言模型和知识图谱，主动协助研究人员发现相关文献。用户可用自然语言输入研究问题，系统可检索并返回相关论文。ASK采用检索增强生成（RAG）方法，自动提取关键信息并生成问题答案。我们对ASK进行了可用性与实用性评估，结果表明系统操作简便，用户普遍满意。
原文摘要 · Abstract (English)
As the volume of published scholarly literature continues to grow, finding relevant literature becomes increasingly difficult. With the rise of generative Artificial Intelligence (AI), and particularly Large Language Models (LLMs), new possibilities emerge to find and explore literature. We introduce ASK (Assistant for Scientific Knowledge), an AI-driven scholarly literature search and exploration system that follows a neuro-symbolic approach. ASK aims to provide active support to researchers in finding relevant scholarly literature by leveraging vector search, LLMs, and knowledge graphs. The system allows users to input research questions in natural language and retrieve relevant articles. ASK automatically extracts key information and generates answers to research questions using a Retrieval-Augmented Generation (RAG) approach. We present an evaluation of ASK, assessing the system's usability and usefulness. Findings indicate that the system is user-friendly and users are generally satisfied while using the system.
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