Knowledge Graph

The foundational data model is the RDF triple: subject-predicate-object. This forms a directed, labeled graph where:

• Nodes represent real-world entities (people, places, concepts) or literal values.
• Edges represent named, directed relationships between those entities. This structure enables the representation of complex, non-hierarchical connections that relational databases struggle to model efficiently.

Unlike a simple property graph, a true knowledge graph relies on a formal ontology (often expressed in OWL or RDFS) to define:

• Classes: The types of entities (e.g., schema:Person, schema:Organization).
• Properties: The types of relationships (e.g., schema:worksFor, schema:founder). This schema layer enables logical inference, allowing a machine to deduce that if an entity is a schema:CEO, it is also a schema:Person.

Every node and edge is identified by a globally unique, machine-resolvable Uniform Resource Identifier (URI). This is critical for disambiguation:

• The entity Tim Cook is not a string but a URI like https://example.com/entities/tim-cook.
• This URI can be linked to the same entity in external knowledge bases like DBpedia or Wikidata, enabling federated queries and preventing the merging of distinct entities with similar names.

A defining characteristic is the ability to derive new, implicit knowledge from explicit facts using a reasoner. By applying rules to the ontology, the system can infer:

• Transitive relationships: If A is located in B, and B is located in C, then A is located in C.
• Inverse properties: If X employs Y, then Y is employedBy X. This capability transforms a static dataset into a dynamic system that can answer queries based on inferred, rather than just stored, data.

Knowledge graphs are accessed via declarative query languages designed for pattern matching on graph structures, most notably SPARQL (the W3C standard). Unlike SQL, SPARQL allows for:

• Pathfinding queries: Finding all entities connected by a variable-length chain of relationships.
• Federated queries: Splitting a single query across multiple distributed knowledge graph endpoints simultaneously. This enables complex analytical questions like 'Find all suppliers of a critical component located in a high-risk region.'

A knowledge graph is designed to be an open, interconnected system, not a silo. It adheres to Linked Data principles:

• Using standard RDF serializations like JSON-LD or Turtle for data exchange.
• Linking internal entities to external, authoritative identifiers (e.g., linking a corporate entity to its LEI number or a location to its GeoNames ID). This contextual enrichment grounds internal data in the wider world, providing a rich semantic context for AI reasoning.

The process of disambiguating mentions of real-world entities in text and connecting them to unique identifiers within a knowledge graph. Critical for maintaining a high-quality, deduplicated graph.

• Resolves "Apple" the company vs. "apple" the fruit
• Uses named entity recognition (NER) followed by candidate ranking
• Links to authoritative identifiers like Wikidata Q-IDs or DBpedia URIs

The technique of anchoring large language model outputs to deterministic facts stored in a knowledge graph. This prevents hallucination by forcing the model to retrieve and cite verified triples.

• Replaces probabilistic generation with graph traversal for factual queries
• Enables retrieval-augmented generation (RAG) with structured data
• Provides auditable, citation-ready provenance for every claim

Captures the complete, auditable chain of transformations from raw source data to final knowledge graph assertions. Essential for compliance and debugging erroneous facts.

• Records who asserted a triple, when, and from which source
• Uses provenance graphs (DAGs) to visualize dependency chains
• Enables impact analysis when upstream data sources change

The logic for selecting the definitive URI or entity record when multiple variants exist for the same real-world object. Consolidates authority signals and prevents graph fragmentation.

• Merges duplicate nodes like "NYC" and "New York City" into a single canonical entity
• Uses owl:sameAs assertions and deterministic matching rules
• Critical for maintaining a single source of truth in enterprise graphs

Algorithmic evaluation of the quality, relevance, and trustworthiness of sources cited within a knowledge graph. Ensures that graph assertions are backed by authoritative provenance.

• Scores sources based on historical accuracy, domain authority, and recency
• Flags assertions derived from low-confidence or deprecated origins
• Powers automated fact-checking and trust ranking pipelines