Toward Mapping an NGSI-LD Context Model on RDF Graph Approaches: A Comparison Study

Area of Science:

• Computer Science
• Internet of Things
• Semantic Web Technologies

Background:

• Internet of Things (IoT) deployments often lack interoperability due to isolated solutions.
• Standardized data models like Next Generation Service Interfaces-Linked Data (NGSI-LD) aim to bridge this gap.
• NGSI-LD utilizes JSON for Linked Data (JSON-LD) for context information, leveraging semantics and linked open data.

Purpose of the Study:

• To address the theoretical limitations of NGSI-LD graph APIs and query languages.
• To propose standardized mapping mechanisms for NGSI-LD data to RDF graphs.
• To demonstrate the feasibility of semantically exploiting RDF stores for NGSI-LD data processing.

Main Methods:

• Developing standardized mapping mechanisms from JSON-LD payloads to RDF graphs.
• Ensuring compliance with the NGSI-LD context model and API specifications.
• Avoiding the use of reification for relationship annotation in JSON-LD payloads.

Main Results:

• Proposed mapping mechanisms enable semantic exploitation of NGSI-LD data in RDF stores.
• Demonstrated that processing NGSI-LD data semantically using RDF stores is feasible and uncomplicated.
• Illustrated the mapping approaches with real-world use-case examples.

Conclusions:

• Semantically processing NGSI-LD data via RDF stores is achievable and straightforward.
• The proposed mapping mechanisms facilitate enhanced interoperability and data utilization in IoT.
• Further exploitation of semantic approaches can unlock the full potential of NGSI-LD.