concept

graph database

Also known as: graph database, graph databases, Graph DB

Facts (36)

Sources

Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv Aug 7, 2025 6 facts

procedureThe GraphProducer module accepts generic triples generated by the model and transforms them into a property graph format compatible with the target graph database, with future plans to support RDF triple conversion.

claimEven optimized graph databases can struggle with real-time performance when executing multi-hop traversals or subgraph ranking, which hampers interactive use cases.

claimThe RGL library, as described by Li et al. (2025), integrates graph indexing, dynamic node retrieval, and subgraph construction to improve retrieval speed, though its limited support for diverse graph database backends constrains its generalizability in enterprise environments.

procedureThe KGLoader component accepts input in a specified graph data format and loads it into a designated graph database, with different loaders implemented for different destinations for graph visualization, analysis, and production.

referenceThe indexing and retrieval pipeline stores the knowledge graph in both a Vector DB and a Graph DB, using Milvus (Wang et al., 2021) for storing embeddings and iGraph (Csárdi and Nepusz, 2006) for in-memory graph storage.

procedureThe EntityRelationNormalizer performs two key functions: it enables deduplication by normalizing variations of the same entity and relation to be merged into one, and it standardizes entity and relation names to ensure compatibility with the graph database.

Combining Knowledge Graphs With LLMs | Complete Guide - Atlan atlan.com Atlan Jan 28, 2026 5 facts

procedureMaintaining consistency between graph databases, vector stores, and LLM inference infrastructure requires monitoring data freshness, handling partial failures, and implementing retry logic for transient errors.

claimGraph database infrastructure for knowledge graph-LLM integration requires storage for entities and relationships, with examples including Neo4j, JanusGraph, and cloud-native options.

claimAI governance frameworks must enforce permissions at the graph level to ensure Large Language Models only access relationships authorized for each specific user, which requires integrating graph databases with enterprise identity systems and propagating permissions through query execution.

claimOrganizations report faster implementation timelines when using integrated platforms for knowledge graphs and LLMs compared to assembling separate graph databases, vector stores, and LLM infrastructure.

referenceThe core infrastructure components required for GraphRAG include a graph database for relationship storage, vector embeddings for semantic search, a query planner for graph traversal strategy, and context compression tools to fit results within Large Language Model token limits.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 4 facts

claimNeo4j has integrated natural language processing tools that translate user queries into Cypher, the native graph query language of Neo4j, to increase the accessibility and usability of graph database systems for users without deep technical expertise.

referenceLiang, Tan, Xie, Tao, Wang, Lan, and Qian authored 'Aligning large language models to a domain-specific graph database', an arXiv preprint published in 2024 (arXiv:2402.16567).

perspectiveFuture research in the integration of large language models and knowledge graphs must focus on refining methods for data exchange between graph databases and large language models, improving encoding algorithms to capture fine-grained relationship details, and developing adaptation algorithms for domain-specific graph databases.

referencePokornỳ authored 'Integration of relational and graph databases functionally', published in Foundations of Computing and Decision Sciences in 2019 (Volume 44, Issue 4, pages 427–41).

LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph puppygraph.com PuppyGraph Feb 19, 2026 4 facts

claimLarge Language Models (LLMs) can generate incorrect query statements if they misinterpret a user's natural language question, which leads to deterministic but factually wrong results when executed against a graph database.

measurementPuppyGraph can be deployed in under 10 minutes, which allows users to bypass the cost, latency, and maintenance hurdles associated with traditional graph databases.

claimPuppyGraph allows teams to use existing SQL engines for tabular workloads and PuppyGraph for relationship-heavy analysis on the same source tables, avoiding the need to force all use cases through a graph database.

claimPuppyGraph reduces total cost of ownership by eliminating the need for data pipelines, duplicated storage, parallel governance, and high-memory hardware required by traditional graph databases.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 2 facts

referenceY. Tian provided an industry perspective on the state of graph databases in a 2022 article published in SIGMOD Record.

referenceP.T. Wood authored a paper titled 'Query languages for graph databases' published in SIGMOD Rec.

Context Graph vs Knowledge Graph: Key Differences for AI - Atlan atlan.com Atlan Jan 27, 2026 2 facts

claimKnowledge graphs are built on RDF triple stores or property graphs like Neo4j, whereas context graphs are built on graph databases extended for operational and AI context.

claimModern data platforms are increasingly supporting both knowledge graph and context graph capabilities through unified architectures, extending graph databases with active metadata collection, temporal storage, and policy enforcement.

Top 10 Use Cases: Knowledge Graphs - Neo4j neo4j.com Neo4j Feb 1, 2021 2 facts

claimGraph databases are flexible in adding new kinds of relationships and adapting data models to new business requirements, in addition to storing relationships between data points.

claimUtilizing a graph database improves access to information, enabling users and customers to find the products, services, or digital assets they need.

bureado/awesome-software-supply-chain-security - GitHub github.com GitHub 2 facts

referenceGUAC (guacsec/guac) is a tool that aggregates software security metadata into a high-fidelity graph database.

referenceArcadeDB is an open-source (Apache 2.0) multi-model graph database used for supply chain traceability, supporting multi-tier provenance tracking via native graph traversal, supplier relationship graphs, and real-time anomaly detection across complex supply networks.

Knowledge Graphs vs RAG: When to Use Each for AI in 2026 - Atlan atlan.com Atlan Feb 12, 2026 2 facts

claimGraph databases excel at complex path queries, such as identifying which suppliers serve competitors who recently entered a market, because these queries require traversing multiple relationship hops.

referenceGraphRAG infrastructure requires graph databases (such as Neo4j or Amazon Neptune), vector stores (such as Pinecone or Weaviate), and integration layers connecting both components.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 2 facts

procedureTo store triple hypernodes in current graph databases, objects are connected to their corresponding hypernodes labeled with the full meaning of the hypernodes, while maintaining the relationship paths.

Enhancing LLMs with Knowledge Graphs: A Case Study - LinkedIn linkedin.com LinkedIn Nov 7, 2023 1 fact

procedureTo build the ontology, the authors identified entities and relationships from raw document text and used the GPT-4 Completion API to programmatically generate files in JSON Lines format for insertion into a graph database.

KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org arXiv Mar 18, 2025 1 fact

claimGraph Retrieval-Augmented Generation (GraphRAG) offers an advantage over traditional RAG systems by retrieving knowledge from graph databases and utilizing triplets as the primary data source.

Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org arXiv Jun 29, 2025 1 fact

referenceGraph databases like Neo4j address the challenge of highly interconnected datasets by efficiently modeling and processing complex, evolving data structures using nodes, relationships, and properties.

LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org arXiv Mar 11, 2025 1 fact

procedureThe graph construction process involves transforming extracted entities and relationships into vector embeddings via embedding models, while simultaneously processing existing entities and relationships from the graph database into vector representations stored in a vector store for efficient retrieval.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 1 fact

claimWu et al. (2013) adopted an ontological knowledge base to extract highly semantically similar sub-graphs in graph databases, allowing for the recommendation of semantically relevant sub-graphs.