K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data

2024-03-06

arXiv.org

AI Summary

Existing data-driven multivariate time series (MTS) graph construction methods suffer from small-sample bias and fail to accurately capture spatiotemporal dependencies, thereby limiting graph neural network (GNN) performance. To address this, we propose a knowledge-enhanced graph construction framework: first, we explicitly model domain priors—such as physical laws—implicitly encoded in large language models (LLMs) as sensor-level knowledge linkage graphs; second, we design a differentiable graph alignment module to semantically fuse knowledge graphs with signal-driven graphs. Our approach enables transfer of generic knowledge to specific MTS scenarios via prompt engineering, knowledge graph extraction, and cross-graph alignment. Extensive experiments on diverse MTS downstream tasks—including classification and forecasting—demonstrate substantial improvements over state-of-the-art GNNs. Quantitative graph structure evaluation further confirms that knowledge injection significantly enhances both discriminability and interpretability of the learned graphs.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Sourced from various sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies, e.g., correlations among sensors. To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data. Typically, existing approaches construct graphs solely from MTS signals, which may introduce bias due to a small training dataset and may not accurately represent underlying dependencies. To address this challenge, we propose a novel framework named K-Link, leveraging Large Language Models (LLMs) to encode extensive general knowledge and thereby providing effective solutions to reduce the bias. Leveraging the knowledge embedded in LLMs, such as physical principles, we extract a extit{Knowledge-Link graph}, capturing vast semantic knowledge of sensors and the linkage of the sensor-level knowledge. To harness the potential of the knowledge-link graph in enhancing the graph derived from MTS data, we propose a graph alignment module, facilitating the transfer of semantic knowledge within the knowledge-link graph into the MTS-derived graph. By doing so, we can improve the graph quality, ensuring effective representation learning with GNNs for MTS data. Extensive experiments demonstrate the efficacy of our approach for superior performance across various MTS-related downstream tasks.

Problem

• Generating graphs from MTS data to capture spatial-temporal dependencies effectively
• Reducing biases in graph generation caused by small training datasets
• Leveraging LLM knowledge to enhance graph quality for MTS representation learning

Innovation

• Leveraging LLMs to generate knowledge-link graphs for sensors
• Aligning knowledge graphs with data-driven graphs to reduce bias
• Enhancing MTS graph quality for improved representation learning