Knowledge Graphs
Triples and ontologies, entity resolution, embedding-based link prediction, and grounding LLMs in structure.
6concepts
65flashcards
43minutes of reading
- 01 Building a Graph from Unstructured Text The extraction pipeline from documents to triples, why open extraction produces an unusable graph, and the schema and validation decisions that determine whether the result is queryable.
- 02 Entity Resolution at Graph Scale Why matching records is quadratic and how blocking makes it tractable, the transitivity trap in clustering matches, and why this determines a knowledge graph's quality more than anything downstream.
- 03 Grounding Language Models in Structured Knowledge What a knowledge graph provides that vector retrieval cannot, the two integration patterns, and where the translation from language to structure fails.
- 04 Knowledge Graph Embeddings How representing entities and relations as vectors turns completion into a scoring problem, what each scoring function can and cannot express, and why evaluation in this area has been unreliable.