Ontology-based approach to the Natural Language Understanding (NLU) processing allows to improve questions answering quality in dialogue systems. We describe our NLU engine architecture and evaluate its implementation. The engine transforms user input into the SPARQL SELECT, ASK or INSERT query to the knowledge graph provided by the ontology-based data virtualization platform. The transformation is based on the lexical level of the knowledge graph built according to the Ontolex ontology. The described approach can be applied for graph data population tasks and to the question answering systems implementation, including chat bots. We describe the dialogue engine for a chat bot which can keep the conversation context and ask clarifying questions, simulating some aspects of the human logical thinking. Our approach uses graph-based algorithms to avoid gathering datasets, required in the neural nets-based approaches, and provide better explainability of our models. Using question answering engine in conjunction with data virtualization layer over the corporate data sources allows extracting facts from the structured data to be used in conversation.
翻译:对自然语言理解(NLU)处理的基于本体学的方法可以提高对话系统中的回答质量。我们描述我们的NLU引擎结构并评估其实施情况。引擎将用户输入转换成 SPARQL SELECT、ASK或INSTERT 查询到本体数据虚拟化平台提供的知识图中。这种转换是基于根据Ontolex本体学建立的知识图的字典水平。所描述的方法可以应用于图形数据人口任务和问题解答系统的实施,包括聊天机。我们描述一个可以保持对话背景和提出澄清问题的聊天机的对话引擎,模拟人类逻辑思维的某些方面。我们的方法使用基于图表的算法来避免收集神经网法所要求的数据集,并提供我们模型的更好解释性。在公司数据源上使用问答引擎与数据虚拟化层一起使用,可以从结构化数据中提取事实,用于对话。