H-4088: Add knowledge graph glossary and tutorial content #6498
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Removes mention of blocks
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area/apps > hash.ai
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vilkinsons
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CiaranMn
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Mar 4, 2025
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| In LPGs, both entities and links can have any number of _properties_, which are key–value pairs storing additional information (for example, a Person node might have properties like `name:"Alice"` and `age:30`). In HASH, both entities and links can contain any number of properties, or other _links_ (allowing links to point to other links, as required). | ||
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| In LPGs and HASH, links are usually **directed**, meaning they have start and end at specific entities, though in some use cases direction can be ignored or traversed in both ways as needed. |
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Suggested change
| In LPGs and HASH, links are usually **directed**, meaning they have start and end at specific entities, though in some use cases direction can be ignored or traversed in both ways as needed. | |
| In LPGs and HASH, links are usually **directed**, meaning that one entity is the source and the other is the target, and which is which is important – though in some use cases direction can be ignored or traversed in both ways as needed. |
| ``` | ||
| CREATE (p:Person { name: "Alice", age: 30 }); | ||
| CREATE (q:Person { name: "Bob", age: 32 }); | ||
| CREATE (p)-[:Friends With]->(q); |
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Not a suggestion but just to note that in a directed graph, this example means that Alice is friends with Bob but Bob is not known to be friends with Alice (which is a possible real-world situation!)
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| ### Use cases and benefits | ||
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| Property graph knowledge graphs are common in **enterprise** settings where performance and agility are priorities. They are well-suited for operational applications like fraud detection, recommendation, or supply chain analysis where you need to traverse and query complex relationships quickly. Graph databases implementing LPG can perform graph traversals and pattern matching efficiently at scale, which may be hard to do with relational databases in such domains. Many organizations build internal knowledge graphs using property graph models to achieve a unified view of their business data (customers, products, transactions, etc.) without having to predefine a rigid global schema. For instance, a **supply chain knowledge graph** can use a property graph to model suppliers, factories, shipments, parts, and so on, with various attributes for each, enabling fast queries like “find alternative suppliers for component X in region Y” or “which products would be affected if supplier Z has a delay?”. (We will see a detailed supply chain example later.) Property graphs also lend themselves to integration with graph analytics and algorithms (community detection, shortest paths, centrality, etc.), supporting advanced **network analysis** on enterprise data. The benefit of this type is often *pragmatic flexibility*: teams can start populating a graph and iteratively add properties or new types of nodes as needed, aligning with agile development. |
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I'm not sure it's important but I don't have a good intuition as to what 'agility' means in this context (first sentence).
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🌟 What is the purpose of this PR?
Adds glossary and tutorial pages explaining knowledge graphs. Small drive-by improvement to the user guide, and in-repo READMEs/documentation.
The two
WIP_prefixed glossary pages will be finished off separately with time (Linked Property Graphs and Event-Driven Knowledge Graphs). Both will be useful in context of our forthcoming open-source push, and user conversations around Safeguarded AI beta testing.