How to build a Knowledge Graph from scratch
A Knowledge Graph is an interconnected set of data, entities, and relationships that are designed to provide insights and decisions for an organization. It is typically used for tasks such as data integration, data enrichment, and analytics. And it’s a perfect fit for any enterprise.
Building a knowledge graph can be a daunting task.
It requires a thorough understanding of the domain, the data, and the technology used to construct the graph.
There is no one-size-fits-all solution, each data challenge is unique. But we provide you here an overview of the basics needed to build a successful Knowledge Graph, and the questions to focus on.
Step 1: Define your business needs
The first step in building a Knowledge Graph is to gather requirements from stakeholders. This includes understanding the needs of the organization, the datasets that need to be integrated, and the type of insights that need to be provided.
❑ What would you do if you had any data you need?
❑ What domain needs to be improved?
❑ What do you want to uncover, predict or estimate?
❑ Who is the intended audience of the data?
❑ What ethical considerations must be taken into account?
❑ What type of queries do you expect users to make?
Step 2: Collect Data
Identify the entities that you want to connect to start thinking about how they might be related. These entities can be anything from people, places, things, or concepts. Then collect relevant data. This data should be relevant to the domain, it can include structured or unstructured data (public databases, web scraping, and proprietary data sources…).
❑ Which data is relevant?
❑ Where is the data and is it available?
❑ What type of data is relevant?
❑ What is the quality of the data?
Step 3: Model the data
Once the data is collected, it needs to be modelled. This involves creating a schema that captures the structure of the data and the set of rules that determines how the entities will be connected. This schema should include the entities, the types of relationships that exist between entities, the properties of each entity and the rules for how they should be connected.
Once you have a schema, you can start to model the data. This involves creating a graph database that stores the entities and the relationships between them. There are several different tools available for this, such as Neo4j and Apache TinkerPop.
❑ How should the data be preprocessed before modeling?
❑ Is the data sufficient to create a reliable model?
❑ What data should be excluded from the model?
❑ How will the model be evaluated and validated?
❑ What resources are needed to implement the model?
Step 4: Store the data
Now that the data has been modelled, it needs to be stored in a repository.
This includes selecting the right graph database, or file system, query language, and data integration tools.
The choice of repository and the technology stack will depend on the requirements of the knowledge graph, it is important to choose the right one.
❑ What techniques should be used to analyse the data?
❑ What query language does the graph database use?
❑ What security measures are in place to protect the data?
❑ What scalability features does the graph database offer?
❑ How easy is it to migrate data to the graph database?
Step 5: Design the Knowledge Graph
Now you can design your Knowledge graph.
This involves creating the nodes, edges, and rules that will govern the behaviour of the graph. This should be done in collaboration with stakeholders, to ensure that the graph meets the needs of the organization.
❑ What query language will be used?
❑ What scalability features will offer the Knowledge Graph?
❑ What security measures will be taken to protect sensitive data?
Step 6: Use the Data
Once your data is loaded, you can query and analyse it. This allows you to retrieve information from the graph and use it in your applications or analyses. To load the data into the Knowledge graph, you can do so manually or by using a data integration tool.
❑ How will the user be able to interact with the knowledge graph?
❑ How will the data be visualized?
❑ How can the data be interpreted?
❑ How can the data be verified for accuracy?
❑ Are there any ethical considerations when using the data?
Step 7: Monitor and Maintain
The final step is to monitor and maintain the knowledge graph. This includes regularly updating the data, ensuring the accuracy of the data, and making sure that the graph is performing as expected.
❑ How will the knowledge graph be updated?
❑ What measures will be taken to ensure data accuracy and consistency?
❑ What kind of data transformation is needed to integrate the data sources?
❑ What methods should you use to clean the data?
❑ How do you handle changes and versioning in the knowledge graph?
❑ What metrics should be used to measure the performance of the knowledge graph?
❑ What processes should be in place for troubleshooting and resolving issues?
Building a Knowledge Graph from the ground up can be complex, but it is a valuable tool for making sense of large datasets and discovering connections between entities. By following these steps, you can create a knowledge graph that is reliable, extensible, and scalable.
The best part about using a knowledge graph is that it can be added to existing architectures without disrupting the system. If you already have a data management system and database in place, and your professional teams don’t want to lose their efforts, we can help you find the most effective solution to include the benefits of a knowledge graph without destroying existing assets.
Building and using a knowledge graph is also a way to think about data management with more openness, sharing, and accessibility.
The right technology can create a powerful resource for knowledge discovery and decision-making. You can also easily integrate external data, giving you a comprehensive and up-to-date view of your business. This gives you the opportunity to improve the entire knowledge sharing among your employees.