家庭服务机器人领域知识图谱构建与应用

Domain Knowledge Graph Construction and Application of Home Service Robot

对于家庭服务机器人,能否准确快速地获取到家庭场景中实体的语义信息是决定其智能化水平的关键。为了增强其语义信息获取能力与知识推理能力,针对家庭场景提出了一种面向服务机器人的领域知识图谱自动化构建流程。首先,利用词频-逆向文件频率算法(Term Frequency–Inverse Document Frequency,TF-IDF)从文本信息中提取服务策略关键字,构建服务策略图谱;其次,通过预训练的场景分割模型(Scene Segmentation Model,SSM)识别出场景内的实体;之后,根据当前场景的实体信息,运用场景分类模型(Scene Classification Model,SCM)来预测房间类别,生成结构化数据;再次,根据结构化数据构建家庭实体图谱;最后,将服务策略图谱与家庭实体图谱合并为家庭服务领域知识图谱,并将其存储到neo4j图数据库中。实验结果表明,所提出的方法可以根据非结构化数据自动生成领域知识图谱,通过查询知识图谱,检索家庭服务所需的语义信息,可以帮助机器人生成适用于当前工作环境的服务策略,使其更加智能地完成服务任务,证明了方法的可行性。

For the home service robot,whether it can accurately and quickly obtain the semantic information of the entity in the home scene is the key to determine its intelligence level.In order to enhance its semantic information acquisition and knowledge reasoning ability,we propose an automatic construction process of knowledge graph based on family scene.Firstly,TF-IDF algorithm is used to extract service policy keywords from text information and construct the service policy graph.Secondly,the pre-trained scene segmentation model(SSM)is used to identify the entities in the scene,and the scene classification model(SCM)is used to predict the room category and generate structured data.Then,the family entity map is constructed according to the structured data.Finally,the service policy graph and the family entity graph are merged into the family service knowledge graph and stored in the Neo4J graph database.The experimental results show that the proposed method can automatically generate domain knowledge graph according to unstructured data.By querying the knowledge graph,the semantic information needed by service can be searched,and the service policy suitable for the current working environment can be generated by the robot,the robot can complete the service more intelligently,which proves the effectiveness of the proposed method.