The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology is a well-known and widely used framework for developing data mining and machine learning projects. However, it can also be adapted for developing knowledge graphs. Here is an adjusted version of CRISP-DM for developing knowledge graphs:
Business Understanding: In this stage, the business problem and the use case for the knowledge graph are identified. The stakeholders and their requirements are identified, and the business goals and objectives are established.
Data Understanding: In this stage, the relevant data sources are identified, and the data is collected and analyzed. The data is then explored to identify any patterns or relationships that could be used to develop the knowledge graph and draft ontology with taxonomy.
Data Preparation: In this stage, the data is prepared for the knowledge graph development. This includes data cleaning, data integration, and data transformation. The data is then mapped to a common ontology or schema, which forms the backbone of the knowledge graph.
Knowledge Graph Development: In this stage, the knowledge graph is built using the mapped data and the selected ontology or schema. The graph is enriched with additional knowledge from external sources, such as APIs and Linked Open Data. The quality of the knowledge graph is assessed using various metrics, such as completeness, accuracy, and consistency.
Evaluation: In this stage, the knowledge graph is evaluated against the business requirements and goals established in the first stage. The stakeholders are involved in the evaluation process to ensure that the knowledge graph meets their needs and is fit for purpose.
Deployment: In this stage, the knowledge graph is deployed in the target environment. This may involve integrating the graph with other systems or applications, such as search engines or chatbots. The stakeholders are trained on how to use the knowledge graph, and ongoing support and maintenance are provided.
Monitoring: In this stage, the performance of the knowledge graph is monitored and evaluated over time. This includes tracking metrics such as usage, relevance, and accuracy. Any issues or challenges that arise are addressed through ongoing support and maintenance.
Overall, the CRISP-DM methodology can be adapted for developing knowledge graphs, with an emphasis on data preparation, ontology or schema mapping, and evaluation against business goals and objectives.
