A knowledge-guided hybrid learning framework for semantic constraint integration in time series models
Publication date
2025-10-31
Secondary publication date
2025-12-12
Document type
Conference paper
Author
Organisational unit
Conference
24th International Semantic Web Conference (ISWC 2025) ; Nara, Japan ; November 2–6, 2025
Publisher
RWTH
Series or journal
CEUR Workshop Proceedings
ISSN
Periodical volume
4085
Book title
ISWC-C 2025 : industry, doctoral consortium, posters and demos at ISWC 2025
First page
125
Last page
135
Part of the university bibliography
✅
Language
English
Abstract
Current time series models often operate solely on sensor data, lacking the contextual understanding that domain knowledge provides. This limitation particularly exists in domains like maritime operations or medical monitoring, where sensor data are often noisy, incomplete, or ambiguous. To address this gap, this doctoral research proposes a hybrid learning framework that integrates semantic knowledge from ontologies, domain texts, and expert-defined rules into the modeling process as formal constraints. The framework comprises three main building blocks: (1) learning joint representations from heterogeneous sources such as time series, structured knowledge, and unstructured text; (2) extracting and formalizing semantic knowledge into symbolic or functional constraints; and (3) fusing these components into a hybrid framework, where formal constraints complement machine-learned patterns. Initial work has been conducted in the maritime domain and will be extended to medical datasets for cross-domain evaluation.
Description
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0).
Version
Published version
Access right on openHSU
Open access