AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes XGAP, an ambiguity-aware cross-platform graph query planner that integrates language models to generate structural interpretations and compiles them directly into distributed, compatible, and executable optimized plans.
KGVoyager is presented, a KG-agnostic agentic architecture that generates SPARQL queries from natural language questions by dynamically discovering graph structure and semantics, requiring only a query endpoint of the underlying graph.
PoP-RAG is proposed, which features a passage-on-edge (PoE) graph that links the passages with graph edges to resolve the one-to-many entity-passage mapping problem and enables targeted retrieval for each sub-query and ensures transparent response derivation logic, thus enhancing explainability.
Retrieval-Augmented Generation (RAG) effectively grounds large language models (LLMs) in external knowledge but struggles with exploratory reasoning problems (ERPs) that are the complex queries involving high uncertainty and ambiguity. Resolving ERPs requires complex reasoning with unclear paths, tending to result...
Preliminary experiments with the proof-of-concept prototype suggest that querying graph data with ontological knowledge may finally be within reach.
Generating executable structured queries for OpenStreetMap (OSM) from natural language remains a challenging task due to the rigid syntax of OverpassQL and the steep learning curve it presents to end-users. While steering Large Language Models (LLMs) via demonstration contexts offers a promising solution, existing...
Conjunctive regular path queries (CRPQs) form a critical backbone of modern graph query languages, integrating subgraph matching with regular path queries (RPQs). Despite their ubiquity in applications like social networks, finance, and scientific data analysis, CRPQ optimization lacks a unified framework, relying inst...
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