With the help of Ontotext’s knowledge graph technology experts, we have compiled a list of 10 steps for building knowledge graphs. Each of them takes time and needs careful consideration to meet the goals of the particular business case it has to serve. As a result, a knowledge graph created with a view to a specific context and business data needs opens vast opportunities for smart data management.
Although more and more organizations in various industries turn to knowledge graphs for better enterprise knowledge management, data and content analytics, there is no universal approach to building them. After working with many clients and on many research projects to help organizations transform and interlink their data into coherent knowledge, we have outlined the following 10 steps:
|1. Clarify your business and expert requirements: Establish the goal behind collecting the data and define what questions you want to be answered.||2. Gather and analyze relevant data: Discover what datasets, taxonomies and other information (proprietary, open or commercially available) would serve you best to achieve your goal in terms of domain, scope, provenance, maintenance, etc.|
|3. Clean data to ensure data quality: Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc.||4. Create your semantic data model: Analyze thoroughly the different data schemata to prepare for harmonizing the data. Reuse or engineer ontologies, application profiles, RDF shapes or some other mechanism on how to use them together. Formalize your data model using standards like RDF Schema and OWL.|
|5. Integrate data with ETL or virtualization: Apply ETL tools to convert your data to RDF or use data virtualization to access it via technologies such as NoETL, OBDA, GraphQL Federation, etc.||6. Harmonize data via reconciliation, fusion and alignment: Match descriptions of one and the same entity across datasets with overlapping scope, handle their attributes to merge the information and map their different taxonomies.|
|7. Architect the data management and search layer: Merge different graphs flawlessly using the RDF data model. For locally stored data GraphDB™ can efficiently enforce the semantics of the data model via reasoning, consistency checking and validation. It can scale in a cluster and synchronize with search engines like Elasticsearch to match the anticipated usage and performance requirements.||8. Augment your graph via reasoning, analytics and text analysis: Enrich your data extracting new entities and relationships from text. Apply inference and graph analytics to uncover new information. Now your graph has more data than the sum of its constituent datasets. It is also better interconnected, which brings more content and enables deeper analytics.|
|9. Maximize the usability of your data: Start delivering the answers to your original questions through different knowledge discovery tools such as powerful SPARQL queries, easy to use GraphQL interface, semantic search, faceted search, data visualization, etc. Also, ensure that your data is FAIR (findable, accessible, interoperable and reusable).||10. Make your KG easy to maintain and evolve: Finally, after you have crafted your knowledge graph and people have started using it, keep it live by setting up your maintenance procedures – the way it would evolve and updates from the different sources will be consumed while maintaining high data quality.|