Federated Graph RAG

The Federated Query Planner is the system's control module that decomposes a natural language query into a retrieval plan spanning multiple, independent knowledge graphs. It determines:

• Which sub-queries to send to which graph based on schema awareness.
• How to combine partial results from different sources.
• The execution order to minimize latency and maximize completeness. This component is critical for maintaining query efficiency and correctness in a decentralized environment where no single graph has a global view.

This component creates semantic mappings between the ontologies of different knowledge graphs. Since graphs are built independently, the same real-world entity (e.g., 'Customer') may be represented by different class names (Customer vs Client) or relationship paths. The mediation layer:

• Uses ontology matching techniques to align classes and properties.
• Translates queries and results between different schema representations.
• Resolves conflicts when data from different sources contradicts. Without this layer, federated retrieval would fail due to semantic incompatibility.

This engine executes the federated query plan by retrieving relevant, connected subgraphs from each participating knowledge graph in parallel. Its key functions include:

• Multi-hop traversal within each local graph to gather context.
• Applying access control policies specific to each data source.
• Returning subgraphs in a standardized format (e.g., RDF triples, property graph JSON) for downstream processing. Performance is paramount, as latency is additive across networks; techniques like connection pooling and request batching are often employed.

After retrieving subgraphs from multiple sources, this component merges them into a single, coherent context for the language model. This involves:

• Entity resolution across graphs to identify and merge nodes representing the same real-world entity.
• Fact consolidation to handle overlapping or complementary information.
• Conflict resolution using predefined rules (e.g., source priority, timestamp) when facts disagree.
• Redundancy elimination to avoid overwhelming the LLM's context window with duplicate triples. The output is a unified, deconflicted knowledge graph segment ready for prompt injection.

A secure, trusted execution environment (TEE) or cryptographic protocol that enables retrieval and computation over sensitive data without exposing the raw graphs. This is essential for regulated industries. Techniques include:

• Secure Multi-Party Computation (MPC) for privacy-preserving query answering.
• Homomorphic Encryption to perform operations on encrypted query embeddings.
• Differential Privacy to add statistical noise to aggregated results. This component ensures data sovereignty is maintained for each graph owner while still enabling collaborative retrieval.

The central coordinator that manages the lifecycle of a federated retrieval session. It handles:

• Service discovery and health checks for all participating graph endpoints.
• Session state tracking across potentially long-running, multi-step queries.
• Failure recovery and fallback strategies if a participant graph is unavailable.
• Audit logging for compliance, recording which graphs were queried and what data was accessed. This manager provides the reliability and observability required for enterprise-grade deployment of the federated system.

Enables unified querying across separate corporate knowledge graphs, such as those from a parent company and its subsidiaries or joint venture partners. This allows for intelligence synthesis without merging sensitive data.

• Key Application: Mergers & Acquisitions due diligence, where legal and financial entities must be analyzed across both companies' proprietary graphs.
• Technical Challenge: Requires graph alignment to map equivalent entities (e.g., 'Customer_ID' in one system to 'Client_Number' in another) and schema mediation to resolve ontological differences.

Facilitates collaborative medical research by querying across hospital EHR knowledge graphs, genomic databases, and pharmaceutical research graphs. Patient data remains at each institution, complying with regulations like HIPAA and GDPR.

• Key Application: Identifying patient cohorts for clinical trials by finding matches across multiple hospital graphs based on anonymized phenotypic and genetic markers.
• Privacy Mechanism: Uses federated learning principles where only encrypted query results or aggregated subgraphs are shared, never raw patient records.

Allows banks and financial institutions to detect sophisticated, cross-institutional fraud patterns. Each bank maintains its own private graph of accounts, transactions, and entities, while the federated system can identify suspicious pathways connecting them.

• Key Application: Uncovering coordinated money laundering rings that use accounts across multiple banks to obscure trails.
• Retrieval Method: Employs multi-hop retrieval across the federated network to trace funds, while differential privacy techniques add noise to queries to prevent reverse-engineering of individual bank data.

Supports intelligence analysis by retrieving from classified knowledge graphs held by different agencies (e.g., CIA, NSA, DIA) or allied nations. Data sovereignty and compartmentalization are paramount.

• Key Application: Threat assessment by correlating entity relationships from signals intelligence (SIGINT) graphs with human intelligence (HUMINT) graphs.
• Security Model: Operates on a need-to-know retrieval basis, often using secure multi-party computation (MPC) to execute queries without exposing the underlying graph structure of each participant.

Provides end-to-end visibility by querying the knowledge graphs of multiple suppliers, manufacturers, and logistics providers. Each company's graph details its internal operations, parts, and inventory.

• Key Application: Predicting and mitigating disruption by identifying single points of failure or bottlenecks across the entire supply network.
• Integration Challenge: Relies on semantic data fabrics and shared upper-level ontologies (e.g., for parts, locations, dates) to enable meaningful federated queries across heterogeneous industrial data models.

Enables researchers to query across distributed knowledge graphs publishing scientific findings, chemical compounds, or astronomical data. This accelerates discovery by connecting findings across labs and publications.

• Key Application: Drug discovery, where queries span pharmacological interaction graphs, genomic variant graphs, and published literature graphs.
• Technical Foundation: Often builds upon linked open data (LOD) principles and uses SPARQL federated query endpoints, with the RAG layer translating natural language into these distributed queries.

A knowledge graph is a structured knowledge base that represents information as a network of entities (nodes) and their relationships (edges), often enhanced with an ontology (schema). It provides a semantic framework for data, enabling complex queries and logical inference. In the context of RAG, it serves as the deterministic source of facts.

• Core Components: Entities, relationships, properties, and an ontology.
• Formats: Common representations include RDF triplestores and labeled property graphs.
• Role in RAG: Acts as the authoritative, structured retrieval source to ground language model outputs.

Federated learning is a decentralized machine learning paradigm where a global model is trained across multiple client devices or data silos without exchanging raw data. Instead, only model updates (e.g., gradients) are shared and aggregated on a central server.

• Key Principle: Data locality; raw training data never leaves its source.
• Primary Benefit: Enables model training on sensitive, distributed datasets while preserving privacy.
• Contrast with Federated Graph RAG: Federated learning focuses on decentralized training, while Federated Graph RAG focuses on decentralized retrieval across multiple knowledge graphs.

Subgraph retrieval is the process of extracting a relevant, connected subgraph from a larger knowledge graph in response to a query. This preserves the local network of entities and relationships, providing rich, structured context to a language model.

• Process: A query identifies seed nodes; algorithms then traverse relationships to fetch neighboring nodes and edges.
• Advantage over Vector Search: Returns interconnected facts, not just isolated text chunks, enabling multi-hop reasoning.
• Role in Federated Setting: In Federated Graph RAG, subgraph retrieval occurs locally within each decentralized graph before results are federated.

Vector-graph hybrid search is a retrieval technique that combines semantic similarity search over vector embeddings with structured pattern matching over a knowledge graph. This improves both recall (via vectors) and precision (via graph structure).

• How it Works: A query is processed to find semantically similar entity embeddings (vector search) and to match specific graph patterns (e.g., SPARQL queries).
• Benefit: Mitigates the limitations of pure keyword or pure vector search by leveraging both meaning and explicit relationships.
• Federated Application: Each node in a Federated Graph RAG system may use hybrid search internally before contributing to a unified result set.

A semantic data fabric is an architectural framework that uses a knowledge graph as a unifying semantic layer to provide integrated, contextualized, and accessible data across an entire organization. It abstracts underlying data sources.

• Core Function: Provides a unified, graph-based view of disparate data silos (databases, APIs, files).
• Relation to Federated Graph RAG: A semantic data fabric can be seen as the underlying infrastructure that creates and manages the decentralized knowledge graphs queried by a Federated Graph RAG system. The RAG architecture operates on top of this fabric.

Neuro-symbolic AI is an approach that integrates neural networks (for perception, pattern recognition) with symbolic systems (for reasoning, logic, and knowledge representation). It aims to combine the learning power of neural models with the transparency and rigor of symbolic AI.

• Key Integration: Neural components process unstructured data; symbolic components (like knowledge graphs) handle rules and facts.
• Role in RAG: Federated Graph RAG is a neuro-symbolic architecture: the language model (neural) generates text, while the federated knowledge graphs (symbolic) provide verifiable, logical constraints.
• Benefit: Enables more robust, interpretable, and factually grounded reasoning than purely neural approaches.