Knowledge Graph Construction: Architectural Pipeline
A Knowledge Graph (KG) integrates information into an ontology-based network of entities and relationships, providing a structured foundation for reasoning and retrieval.
The pipeline begins with Knowledge Extraction From Text (KE).
- Source Diversity: Ingesting structured (SQL), semi-structured (JSON), and unstructured (Markdown/PDF) data.
- NLP Pipeline: Tokenization \to NER \to Relation Extraction \to Triple Generation.
2. Disambiguation and Entity Linking (EL)
A KG must resolve multiple mentions of the same entity to a single unique node.
- Coreference Resolution: Linking "he" or "the company" back to the original entity in the document.
- Canonicalization: Ensuring "IBM," "International Business Machines," and "Big Blue" all map to the same node.
- Concrete Tool: Using Wikidata or a private corporate Ontology as the ground-truth namespace.
3. Graph Storage and Schema
- RDF (Resource Description Framework): Standard for the semantic web. Uses SPARQL for querying.
- LPG (Labeled Property Graph): Standard for industrial graphs (e.g., Neo4j). Entities and edges can have properties (key-value pairs).
- Ontology Enforcement: Using RDFS or OWL to define constraints (e.g., "The
founder_of relation must connect a Person to an Organization").
4. Graph Embeddings and Reasoning
To make the graph useful for AI, we convert nodes into vectors.
- TransE / RotatE: Algorithms that learn embeddings such that
vector(Subject) + vector(Relation) ≈ vector(Object). - Link Prediction: Using embeddings to predict missing relationships (e.g., "There is a 85% probability that Person A knows Person B based on their mutual connections").
- GraphRAG: A hybrid pattern where the LLM retrieves a "sub-graph" of related entities from the KG to answer complex multi-hop questions (e.g., "Which products from Company X are affected by the new EU regulation?").
See Also: