Knowledge Graph Creation
Knowledge Graph Creation involves constructing a graph that encodes entities and their inter-relations present in the source data. When the graph doesn't just store raw data, nodes and relationships, but also describes what the data means, it becomes a knowledge graph. Semantic meaning is added to the data by adding labels to nodes and types to relationships, as well as hierarchical structures to the data.
This involves identifying entities, relationships, and semantic links across chunks and connecting them in a structured, machine-readable graph format and entails some form of data modeling and extraction.
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Defining Ontology: First, decide what entity types and relationship types are relevant. For example, in an e-commerce scenario, entity types might be Customer, Order, Product and relationships might be PLACED (Customer -> Order) or CONTAINS (Order -> Product). This schema design can be done manually with domain experts. A good ontology strikes a balance between being comprehensive and not overly complex.
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Extracting Entities and Relationships: Using the ingested data, the system identifies specific entities and links. Some of this might have already been started in the enrichment phase (entity recognition). For instance, from text “Alice ordered a laptop on Jan 5”, we identify an entity Alice of type Customer, an entity Order123 of type Order, and Laptop of type Product, then create edges: Alice --PLACED--> Order123 --CONTAINS--> Laptop. This extraction can be done via rules or increasingly via LLM prompts that transform text into structured triples (for example, you can prompt the LLM: “read this paragraph and output triples (subject, predicate, object)”).
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Ingesting into a Graph Database: The identified nodes and edges are then inserted into a graph database (like Memgraph, Neo4j or others). Each node can have attributes (metadata) and each edge represents a factual relationship. For example, Order123 might have an attribute
date: 2023-01-05
. Tools and libraries for graph databases often provide APIs to create nodes/edges programmatically. -
Ensuring Data Quality and Consistency: Graphs are only as useful as they are accurate. This means resolving duplicates (is “Alice B.” the same as “Alice Brown”? Should they be one node or two?), handling incomplete data (an Order with no date could be filled if known) and maintaining consistency with source of truth. Building a KG can be resource-intensive and there might be a need for human curation or iterative refinement to get right.
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Leveraging Existing Graphs or Ontologies: Sometimes, rather than building from scratch, you can integrate with existing knowledge bases. For example, linking product names to an existing product catalog graph or using schema.org or domain ontologies as a starting framework. Also, certain LLM-driven tools can help bootstrap a KG by reading documents and proposing relationships.
If a Knowledge Graph is built, it will live in a Graph Database as part of the Data Storage and will be utilized during retrieval and orchestration. In summary, Knowledge Graph Creation structures the domain knowledge explicitly. It transforms textual content into a web of entities and facts, providing a powerful substrate for logical querying and reasoning that complements the pattern-matching ability of language models.