Our firm’s process of developing intelligent applications with knowledge graphs involves defining the domain knowledge, populating the knowledge graph, and using machine learning and natural language processing techniques to generate insights and recommendations for users. Here are some general steps that can be taken to develop intelligent applications using KGs:

The first step is to identify the use case for the intelligent application. This may include identifying the problem to be solved, the target users, and the specific data sources to be used.

The second step is to develop the ontology and taxonomy to represent the domain knowledge. This involves defining the entities, relationships, and properties that are relevant to the use case.

The third step is to populate the knowledge graph with data from the relevant sources. This may involve integrating data from various sources, such as databases, APIs, and web scraping techniques.

The fourth step is to develop the intelligent application. This may involve using machine learning and natural language processing techniques to analyze the data in the knowledge graph and generate insights and recommendations for users.

Once the application has been developed, the application should be tested to ensure that it is functioning as intended. This may involve testing the accuracy of the insights and recommendations generated by the application, and refining the application to improve its performance.