  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 \textit{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.
