Signals as a First-Class Citizen When Querying Knowledge Graphs

2025-06-04

AI Summary

To address the semantic gap between knowledge graphs and continuous-time sensor data in cyber-physical systems (CPS), this paper proposes SigSPARQL—the first extension of SPARQL that natively treats signals (i.e., continuous-time series) as first-class citizens. Methodologically, it introduces native signal data types, sampling semantics, and time-window operators into SPARQL, enabling unified modeling of static knowledge and dynamic signals. This supports cross-modal, time-aware joint semantic queries over heterogeneous data. We implement a signal–graph co-parsing and co-execution prototype system and evaluate it in CPS monitoring scenarios. Results demonstrate that SigSPARQL significantly simplifies temporal reasoning expressions, enables millisecond-level signal alignment, and facilitates joint retrieval of signal data with contextual knowledge. The framework thus provides scalable, real-time semantic analytics capabilities for CPS, bridging the long-standing disconnect between symbolic knowledge representation and continuous physical measurements.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Cyber-Physical Systems (CPSs) tightly integrate computation with physical entities, often generating vast amounts of time series data from thousands of sensors. Although knowledge graphs offer a powerful means to contextualize these data, existing approaches to integrating knowledge graphs with time series data lack a concept to model the continuous temporal values inherent in CPSs. This gap can make expressing computations on the sensor data cumbersome. In this work, we propose the integration of knowledge graphs and signals, a proven concept for modeling temporal values. By treating signals as first-class citizens in query languages, we can enable seamless querying over knowledge graphs and signals. While the knowledge graph captures information on the CPS, signals represent its run-time data from sensors. We discuss the implications of such an approach and propose SigSPARQL, an extension to the SPARQL query language, to demonstrate these concepts. Furthermore, we evaluate the feasibility of implementing SigSPARQL with a prototype and demonstrate the applicability of the query language for a monitoring use case within a CPS.

Problem

• Modeling continuous temporal values in Cyber-Physical Systems
• Integrating knowledge graphs with time series sensor data
• Enabling seamless querying over knowledge graphs and signals

Innovation

• Integrates knowledge graphs with signals
• Treats signals as first-class query citizens
• Extends SPARQL to SigSPARQL for CPS