Volume 13, Issue 7 (July 2026), Pages: 58-67

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 Original Research Paper

Ontology-driven machine learning for marketing activation forecasting

 Author(s): 

Enkhtuul Bukhsuren, Nandin-Erdene Enkhmyagmar, Munkhtsetseg Namsraidorj *

 Affiliation(s):

Department of Information and Computer Sciences, School of Information Technology and Electronics, National University of Mongolia, Ulaanbaatar, Mongolia

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-7767-5311

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.07.005

 Abstract

Marketing activation generates large volumes of data, but these data are often organized in flat tables that do not explicitly capture the relationships among brands, channels, partners, and promotions. As a result, forecasting models may identify statistical associations without fully representing how marketing elements interact in practice. This study addresses this limitation by organizing marketing knowledge through an ontology and implementing it as a knowledge graph. SPARQL queries were used to generate relational indicators that represent interaction patterns among products, promotions, channels, and partners. These semantic features were integrated into regression-based forecasting models. The empirical analysis is based on 2.9 million transactional records from a large FMCG distribution company covering the period 2020–2024. Multiple linear regression, random forest regression, and gradient boosting regression models were developed and compared with baseline models that relied only on conventional tabular features. Model performance was evaluated using RMSE, MAE, and adjusted R². Gradient boosting achieved the best performance (RMSE = 116.93; MAE = 10.15; adjusted R² = 0.27). Although the performance improvements were moderate, models enriched with ontology-derived features consistently outperformed the baseline models. The findings suggest that incorporating structured relational knowledge improves model interpretability and provides incremental predictive gains. The study demonstrates how semantic modeling can be systematically integrated into regression-based marketing activation forecasting in an enterprise context. Future research may apply the proposed framework to independent enterprise datasets to further assess its external validity.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Marketing activation forecasting, Knowledge graphs, Ontology, Regression models, Semantic features

 Article history

Received 15 January 2026, Received in revised form 4 July 2026, Accepted 10 July 2026

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Citation:

Bukhsuren E, Enkhmyagmar NE, and Namsraidorj M (2026). Ontology-driven machine learning for marketing activation forecasting. International Journal of Advanced and Applied Sciences, 13(7): 58-67

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