Optimizing Subgraph Extraction for Dynamic Question Answering
Explores subgraph extraction techniques in knowledge-enhanced retrieval.
Provides formal analysis of computational complexity under varying graph connectivity patterns.
Presents novel algorithms with provable guarantees for real-time traversal.
Combines classic algorithms like A* with newer methods like Beam Search and WaterCircles, dynamically adapting to constraints.
Offers polylogarithmic time complexity and stability metrics, ensuring consistent response times and bounded degradation.
Processes batches of graph updates or queries in parallel to minimize latency for high-frequency applications.
Functional tree structures (like C-trees) and memory-optimized representations enable fast updates and queries on large graphs.
Coupled with stream-processing frameworks (Kafka, Flink) for instantaneous pattern flagging, recommendations, or context updates.
Benchmarking on HotpotQA & TriviaQA shows reduced sensitivity to graph constraints and improved robustness for LLM agents.
Systematic testing on benchmark datasets quantifies stability and accuracy under different constraints.
State-of-the-art performance on commodity hardware, using less memory and running faster than previous methods.
Dynamically maintains a tunable trade-off between completeness and relevance for different query types.