Year: 2026
Type: conference-paper
Abstract: In distributed and constrained Web of Things (WoT) infrastructures, servients exchange RDF messages that can rely on compact formats to reduce both bandwidth and memory footprint. To this end, in the context of the CoSWoT project, we adapted the CBOR-LD specification [1], [2]. As servients can connect or appear in these infrastructures, each using a different dialect, there is a need for off-the-shelf interoperability during message exchanges at runtime. This paper presents a frugal workflow to detect semantic similarity among messages encoded in CBOR-LD using heterogeneous vocabularies. We experimented different ML-based approaches: a lightweight neural network, a decision tree-based model and a transformer-based architecture. We present a end-to-end pipeline able to integrate each of these algorithms and provide alignment predictions. We ran our experiments on a catalogue of more than 700 production payloads curated to train the models. Our results show various levels of prediction accuracy regarding approaches and model sizes, leading to appropriate choices depending on available resources on targeted platforms.
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7127635157