Knowledge-based Research on Automation Engines

Currently, most existing no-code platforms rely mainly on simple rule reasoning, which greatly limits the ability to handle complex business logic and dynamic environments. This study proposes a method to overcome this limitation by combining knowledge graphs and logical reasoning. By introducing a knowledge system, it is possible not only to expand the capabilities of the automation engine, but also to handle more complex inference tasks, by supplementing it with rule-based reasoning, while also retaining the requirements for user-friendliness and meeting individual needs. Knowledge-based systems can better respond to the unknown by adapting to new situations and data. The content of this article is to solve the problem of knowledge assistance rule construction. (1) build a knowledge base with Protégé, and (2) infer the implicit relationships and attributes between data through the reasoning power of the Jena framework. This enhances the semantic understanding and logical reasoning of the data. (3) Through the rule engine of Drools, you can flexibly add or modify rules, and you can combine multiple rules to achieve more complex business logic. In this way, personalized customization can be achieved by combining the general rules in the business and the personalized needs of users.