Making decarbonization queryable: a modular ontology and knowledge graph pipeline for emissions-aware multimodal transport operations

Authors

1 Computer Science Department, Carnegie Mellon University, Pittsburgh, USA 15213
2 Department of Electrical & Computer Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, USA 61801
Corresponding author: eli9@illinois.edu

DOI:

https://doi.org/10.63646/MRKF1328

Keywords:

transport decarbonization, ontology engineering, knowledge graph construction, SHACL validation, emission accounting, provenance, multimodal mobility, FAIR artefacts

Abstract

Transport accounts for roughly a fifth of global greenhouse gas emissions, yet the information needed to manage its decarbonization remains fragmented across timetables, fleet records, telematics, charging infrastructure, energy systems and reporting frameworks. Semantic work in the transport domain has concentrated on interoperability and network modelling, treating emissions as an aggregated by-product rather than as an explicit object of representation. As a result, questions such as which factor, which system boundary and which method produced a given emission figure usually cannot be answered from the data itself. This paper presents the Transport Decarbonization Ontology (TDO), a modular Web Ontology Language (OWL) 2 ontology, together with a reproducible knowledge graph construction pipeline that makes transport emissions queryable at the granularity of individual transport activities. The central design decision is an Activity–Energy–Emission pattern in which an emission estimate is a first-class entity carrying its emission factor, system boundary, greenhouse gas basis, methodology, uncertainty and provenance. TDO reuses established sensor, provenance, temporal, spatial and units vocabularies, and aligns with international transport chain emission accounting concepts. Heterogeneous operational data for a multimodal regional corridor — timetables, vehicle registers, telematics, charging transactions, half-hourly grid carbon intensity and published emission factors — are mapped using the RDF Mapping Language, materialized into an 18.6 million triple graph, validated against 54 Shapes Constraint Language shapes, and queried using SPARQL under an OWL 2 RL profile. Evaluation against 18 competency questions shows complete coverage, compared with 12 and 13 for relational and property graph baselines, with the difference concentrated in method provenance and comparability questions. An ablation confirms that removing estimate reification eliminates that capability. Median query latency stays below four seconds at full scale.

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