Zhao Xuejiao, Chen Huanhuan, Xing Zhenchang, Miao Chunyan
IEEE Trans Neural Netw Learn Syst. 2023 Aug;34(8):4386-4400. doi: 10.1109/TNNLS.2021.3113026. Epub 2023 Aug 4.
Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.
搜索引擎可以根据查询关键词快速响应超链接列表。然而，当查询复杂时，开发人员需要反复提炼搜索关键词并打开大量网页来查找和总结答案。许多问答（Q&A）系统的研究工作试图通过提供简单、准确且易懂的答案来辅助搜索引擎。然而，由于缺乏原始语义上下文，这些答案缺乏可解释性，使得用户难以信任和采用。在本文中，提出了一种基于知识图谱的受大脑启发的搜索引擎助手DeveloperBot，它与人类的认知过程相一致，并且有能力以可解释的方式回答复杂查询。具体来说，DeveloperBot首先通过将复杂的多约束查询拆分为几个有序约束来构建多层查询图。然后，它将约束推理过程建模为受认知科学的扩散激活模型启发的子图搜索过程。最后，提取子图的新特征用于决策。导出相应的推理子图和答案置信度作为解释。决策结果表明，DeveloperBot可以高精度地估计答案和答案置信度。我们实现了一个原型并进行了用户研究，以评估DeveloperBot提供的直接答案和解释是否以及如何满足开发人员的信息需求。
