Building Knowledge Graphs in 10 steps
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The document outlines a 10-step process for building knowledge graphs, starting from defining the goal and gathering relevant data to crafting a semantic data model and integrating it. It emphasizes data quality, harmonization, and the use of ETL tools, as well as the importance of creating usable and maintainable knowledge graphs. The final steps involve leveraging analytics and ensuring the data is fair, ultimately leading to the effective use and evolution of the knowledge graph.
Building Knowledge Graphs in 10 steps
- 1. Building Knowledge Graphs in 10 steps Ontotext Fundamentals How-to Series
- 2. CLARIFY YOUR BUSINESS & EXPERT REQUIREMENTS 1 Establish the goal behind collecting the data and define what questions you want to be answered.
- 3. GATHER AND ANALYZE RELEVANT DATA 2 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.
- 4. CLEAN DATA TO ENSURE DATA QUALITY 3 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.
- 5. CREATE YOUR SEMANTIC DATA MODEL 4 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.
- 6. INTEGRATE DATA WITH ETL OR VIRTUALIZATION 5 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. Generate semantic metadata to make the data easier to update, discover and reuse.
- 7. HARMONIZE DATA VIA RECONCILIATION, FUSION AND ALIGNMENT 6 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.
- 8. 7 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. ARCHITECT THE DATA MANAGEMENT AND SEARCH LAYER
- 9. 8 Enrich your data extracting new entities and 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. AUGMENT YOUR GRAPH VIA REASONING, ANALYTICS AND TEXT ANALYSIS
- 10. 9 Start delivering the answers to your original questions through different knowledge discovery tools such as: ● powerful SPARQL queries ● easy to use GraphQL interface ● semantic and faceted search ● data visualization Ensure that your data is FAIR*. *FAIR: Findable, Accessible, Interoperable and Reusable MAXIMIZE THE USABILITY OF YOUR DATA
- 11. 10 Finally, you have crafted your knowledge graph and people have started using it. Now, keep it live by setting up your maintenance procedures: MAKE YOUR KG EASY TO MAINTAIN AND EVOLVE ● evolve the semantic data model ● consume updates from the different sources ● maintaining high data quality
- 12. To learn more about building, using and maintaining knowledge graphs, read our blog post – Crafting a Knowledge Graph: The Semantic Data Modeling Way THANK YOU!