SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index

Saad Ahmad; Sudhir Attri; Ruchi Dwivedi; Muzamil Yaqoob; Aasim Khan; Praveen Priyadarshi; Raghava Mutharaju

doi:10.1007/978-3-031-75872-0_18

SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index

Saad Ahmad
, Sudhir Attri^*
, Ruchi Dwivedi
, Muzamil Yaqoob
, Aasim Khan
, Praveen Priyadarshi
, Raghava Mutharaju

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Air Quality Index (AQI) is a number aggregated from several air quality sensors deployed in an area. AQI is useful in communicating the air quality to the general public and in making governance decisions to tackle air pollution. However, our ethnographic surveys revealed the existence of a knowledge barrier in interpreting the AQI and data illiteracy in understanding AQI-related charts and trends commonly facilitated by government organizations. This knowledge gap is wider for the marginalized sections of society, who, it turns out, are more exposed to pollution. We use an ontological approach to homogenize the air quality data with social and spatial aspects. The Social Air Quality Index (SAQI) ontology integrates the data from local and central air quality monitoring sensors, meteorological data, and field surveys. This data is converted into a Knowledge Graph, which is used to build an application for civic engagement with the public on pollution to improve community participation of the local institutions and individuals. We evaluated this application through a user survey and received positive feedback. The ontologies, code, and datasets are available under the Apache 2.0 License at https://github.com/kracr/aq-structured-platform.

Original language	English
Title of host publication	Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings
Editors	Wolfgang Maass, Hyoil Han, Hasan Yasar, Nick Multari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	337-354
Number of pages	18
ISBN (Print)	9783031758713
DOIs	https://doi.org/10.1007/978-3-031-75872-0_18
State	Published - 2025
Externally published	Yes
Event	43rd International Conference on Conceptual Modeling, ER 2024 - Pittsburg, United States Duration: 28 Oct 2024 → 31 Oct 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15238 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	43rd International Conference on Conceptual Modeling, ER 2024
Country/Territory	United States
City	Pittsburg
Period	28/10/24 → 31/10/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

AQI
Air Pollution
Community Participation
Data Integration
Ethnography
Knowledge Graph
Ontology
Social AQI

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-75872-0_18

Cite this

Ahmad, S., Attri, S., Dwivedi, R., Yaqoob, M., Khan, A., Priyadarshi, P., & Mutharaju, R. (2025). SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index. In W. Maass, H. Han, H. Yasar, & N. Multari (Eds.), Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings (pp. 337-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15238 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-75872-0_18

Ahmad, Saad ; Attri, Sudhir ; Dwivedi, Ruchi et al. / SAQI : An Ontology Based Knowledge Graph Platform for Social Air Quality Index. Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings. editor / Wolfgang Maass ; Hyoil Han ; Hasan Yasar ; Nick Multari. Springer Science and Business Media Deutschland GmbH, 2025. pp. 337-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index",

abstract = "Air Quality Index (AQI) is a number aggregated from several air quality sensors deployed in an area. AQI is useful in communicating the air quality to the general public and in making governance decisions to tackle air pollution. However, our ethnographic surveys revealed the existence of a knowledge barrier in interpreting the AQI and data illiteracy in understanding AQI-related charts and trends commonly facilitated by government organizations. This knowledge gap is wider for the marginalized sections of society, who, it turns out, are more exposed to pollution. We use an ontological approach to homogenize the air quality data with social and spatial aspects. The Social Air Quality Index (SAQI) ontology integrates the data from local and central air quality monitoring sensors, meteorological data, and field surveys. This data is converted into a Knowledge Graph, which is used to build an application for civic engagement with the public on pollution to improve community participation of the local institutions and individuals. We evaluated this application through a user survey and received positive feedback. The ontologies, code, and datasets are available under the Apache 2.0 License at https://github.com/kracr/aq-structured-platform.",

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author = "Saad Ahmad and Sudhir Attri and Ruchi Dwivedi and Muzamil Yaqoob and Aasim Khan and Praveen Priyadarshi and Raghava Mutharaju",

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Ahmad, S, Attri, S, Dwivedi, R, Yaqoob, M, Khan, A, Priyadarshi, P & Mutharaju, R 2025, SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index. in W Maass, H Han, H Yasar & N Multari (eds), Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15238 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 337-354, 43rd International Conference on Conceptual Modeling, ER 2024, Pittsburg, United States, 28/10/24. https://doi.org/10.1007/978-3-031-75872-0_18

SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index. / Ahmad, Saad; Attri, Sudhir; Dwivedi, Ruchi et al.
Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings. ed. / Wolfgang Maass; Hyoil Han; Hasan Yasar; Nick Multari. Springer Science and Business Media Deutschland GmbH, 2025. p. 337-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15238 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SAQI

T2 - 43rd International Conference on Conceptual Modeling, ER 2024

AU - Ahmad, Saad

AU - Attri, Sudhir

AU - Dwivedi, Ruchi

AU - Yaqoob, Muzamil

AU - Khan, Aasim

AU - Priyadarshi, Praveen

AU - Mutharaju, Raghava

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Air Quality Index (AQI) is a number aggregated from several air quality sensors deployed in an area. AQI is useful in communicating the air quality to the general public and in making governance decisions to tackle air pollution. However, our ethnographic surveys revealed the existence of a knowledge barrier in interpreting the AQI and data illiteracy in understanding AQI-related charts and trends commonly facilitated by government organizations. This knowledge gap is wider for the marginalized sections of society, who, it turns out, are more exposed to pollution. We use an ontological approach to homogenize the air quality data with social and spatial aspects. The Social Air Quality Index (SAQI) ontology integrates the data from local and central air quality monitoring sensors, meteorological data, and field surveys. This data is converted into a Knowledge Graph, which is used to build an application for civic engagement with the public on pollution to improve community participation of the local institutions and individuals. We evaluated this application through a user survey and received positive feedback. The ontologies, code, and datasets are available under the Apache 2.0 License at https://github.com/kracr/aq-structured-platform.

AB - Air Quality Index (AQI) is a number aggregated from several air quality sensors deployed in an area. AQI is useful in communicating the air quality to the general public and in making governance decisions to tackle air pollution. However, our ethnographic surveys revealed the existence of a knowledge barrier in interpreting the AQI and data illiteracy in understanding AQI-related charts and trends commonly facilitated by government organizations. This knowledge gap is wider for the marginalized sections of society, who, it turns out, are more exposed to pollution. We use an ontological approach to homogenize the air quality data with social and spatial aspects. The Social Air Quality Index (SAQI) ontology integrates the data from local and central air quality monitoring sensors, meteorological data, and field surveys. This data is converted into a Knowledge Graph, which is used to build an application for civic engagement with the public on pollution to improve community participation of the local institutions and individuals. We evaluated this application through a user survey and received positive feedback. The ontologies, code, and datasets are available under the Apache 2.0 License at https://github.com/kracr/aq-structured-platform.

KW - AQI

KW - Air Pollution

KW - Community Participation

KW - Data Integration

KW - Ethnography

KW - Knowledge Graph

KW - Ontology

KW - Social AQI

UR - https://www.scopus.com/pages/publications/85209546603

U2 - 10.1007/978-3-031-75872-0_18

DO - 10.1007/978-3-031-75872-0_18

M3 - Conference contribution

AN - SCOPUS:85209546603

SN - 9783031758713

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 337

EP - 354

BT - Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings

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A2 - Han, Hyoil

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PB - Springer Science and Business Media Deutschland GmbH

Y2 - 28 October 2024 through 31 October 2024

ER -

Ahmad S, Attri S, Dwivedi R, Yaqoob M, Khan A, Priyadarshi P et al. SAQI: An Ontology Based Knowledge Graph Platform for Social Air Quality Index. In Maass W, Han H, Yasar H, Multari N, editors, Conceptual Modeling - 43rd International Conference, ER 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 337-354. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-75872-0_18