Editing Knowledge Graphs (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Knowledge graphs represent knowledge as a directed, typed multigraph. Unlike a relational database that describes each entity's attributes in rows and columns, a KG describes the '''web of relationships''' — enabling multi-hop reasoning that relational databases make awkward.

Example: "What are the birthplaces of Nobel Prize winners in Physics who studied at German universities?"

In a relational database, this requires multiple JOINs across tables. In a knowledge graph, it's a graph traversal: find Nobel Physics winners → follow "studiedAt" to universities → filter to Germany → follow "bornIn" to places.

'''The Open World Assumption''': A key semantic difference from relational databases. KGs assume that the absence of a triple does not mean it's false — the information may simply not be recorded. (Closed World Assumption in relational databases: if a row doesn't exist, the fact is false.)

'''Knowledge Graph Embeddings''' (TransE, RotatE, ComplEx) learn dense vector representations of entities and relations, enabling:
* Link prediction: can we predict missing triples?
* Similarity computation: are these entities similar?
* KG completion: enrich an incomplete KG using learned patterns

TransE (a foundational embedding method) represents each relation as a translation in embedding space: h + r ≈ t for each triple (h, r, t). "Paris + locatedIn → France" should hold approximately in the embedding space.

'''Symbolic vs. neural AI''': Knowledge graphs are a form of '''symbolic AI''' — explicit, interpretable, structured representation. Neural models (LLMs) are statistical learners of implicit patterns. The combination — neuro-symbolic AI — is a growing research direction. RAG with a knowledge graph (GraphRAG) retrieves structured facts rather than unstructured text chunks, enabling more precise and verifiable grounding.
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