ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (12): 2501-2513.doi: 10.7544/issn1000-1239.2020.20200750

所属专题: 2020人机混合增强智能的典型应用专题

• 人工智能 • 上一篇    下一篇



  1. (东南大学计算机科学与工程学院 南京 211189) (计算机网络和信息集成教育部重点实验室(东南大学) 南京 211189) (
  • 出版日期: 2020-12-01
  • 基金资助: 

Hybrid Human-Machine Active Search over Knowledge Graph

Wang Meng, Wang Jingting, Jiang Yinlin, Qi Guilin   

  1. (School of Computer Science and Engineering, Southeast University, Nanjing 211189) (Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
  • Online: 2020-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61906037) and CCF-Tencent Open Fund.

摘要: 在知识图谱进行有效的搜索可以为智能问答、语义检索等智能应用提供有效支撑.然而,当用户不能给出明确的查询意图时,一个搜索系统要如何精准捕获用户的兴趣并找到对应的查询目标是项难题.人机混合的主动搜索为缓解用户和机器之间的理解鸿沟提供了桥梁.人机混合的主动搜索核心在于让机器主动地向用户提出相关的问题,从用户的反馈中获取信息,再基于这些信息对检索候选项进行搜索,形成人机混合的回路,最终精准定位用户意图并返回查询结果.在知识图谱表示学习技术的基础上,将知识图谱的搜索任务建模成向量空间中人机混合的主动搜索任务.具体来说,首先将知识图谱和用户的兴趣偏好嵌入到同一低维向量空间.然后,机器主动向用户提问,通过让用户对具体实体进行打分的方式获取相应的反馈信息,进而更新用户偏好在向量空间中的定位.设计了一种评价方式,基于偏好点与其他实体之间的欧氏距离来度量用户对某个实体的兴趣,最终在人机多轮交互后找到对应的目标实体返回给用户.在实验部分,对知识图谱的嵌入过程和主动搜索的过程分别进行了实验,实验结果显示,所提出的方法具有一定的效果.

关键词: 人机混合智能, 知识图谱, 表示学习, 主动搜索 , 语义搜索

Abstract: Effective search over knowledge graphs can provide support for applications such as question answering and semantic search. However, when the user cannot give a clear query, accurately capturing the user’s interest and finding the answer are difficult for machines. Hybrid human-machine active search provides a pathway to bridge the gap between users and machines. Hybrid human-machine active search is a kind of interactive search, and it is originated from the thought of active learning in machine learning field. The core idea is to let the machine issue questions to the user, to obtain information from the user feedback, and then based on this information to eventually capture user intent and return answers. In this paper, we stand on recent advances in knowledge graph representation learning techniques and propose a hybrid human-machine active search in the vector space of a knowledge graph. Specifically, the knowledge graph is first embedded into the low-dimensional vector space, which quantizes the characteristics of entities and relationships, and at the same time, the user’s interests and preferences are embedded into the same space. Then, the machine actively proposes questions to the user, and gets the feedback information by asking the user to rate the specific entity, thus updating the user preference positioning in the vector space. We design an evaluation method to measure the user’s interest in a specific entity based on the Euclidean distance between the preference point and other entities, and finally find the final target entity to return to the user after multiple turns of human-machine interaction. In the experiment part, we conduct experiments on the knowledge graph embedding and the active search respectively, and the experimental results show that the proposed method is effective.

Key words: hybrid human-machine intelligence, knowledge graph, representation learning, active search, semantic search