Ontological Framework for Integrating Predictive Analytics, AI, and

Big Data in Decision-Making Systems Using Knowledge Graph

Stanislav Safranek

and Andrea Zvackova

University of Hradec Kralove, Faculty of Informatics and Management,

Hradecka 1249/6, 50003, Hradec Kralove, Czech Republic

Keywords: Artificial Intelligence, Big Data, Decision System Support, Knowledge Graph, Knowledge-Based Systems,

Predictive Analytics.

Abstract: The rapid development of AI, big data and DSS is changing decision-making processes by enabling the

efficient processing of huge volumes of data for strategic and operational decisions. The increasing

complexity of data-driven decision making requires the integration of predictive analytics, machine learning

and knowledge-based systems. This paper presents an ontological framework that uses a knowledge graph to

systematically depict the interrelationships between these technologies and supports transparent, efficient and

ethical decision making in the areas of business intelligence, healthcare, public policy and crisis management.

It also addresses challenges such as algorithmic bias, ethical considerations and explain ability and highlights

the need for responsible AI deployment.

1 INTRODUCTION

In today’s fast-paced world, breakthroughs in

Artificial Intelligence and Big Data are

revolutionizing how organizations make critical

decisions—enabling real-time predictions, rapid data

processing and effective decision support.

In response to these evolving challenges, we

propose an ontological framework that systematically

represents the intricate interconnections among Big

Data, predictive analytics, Artificial Intelligence,

decision support systems and human-system

integration. This framework, based on a knowledge

graph, provides an approach to understanding how

these components interact and support decision-

making processes across different systematic

domains. Ontology enables organizations to identify

patterns, improve decision-making and increase the

transparency of AI-based decision support systems.

While the rapid evolution of AI and data analytics

fuels groundbreaking capabilities, it also brings to

light serious concerns—such as algorithmic bias,

ethical pitfalls and challenges in AI explainability.

Given the increasing autonomy of intelligent systems,

ensuring fairness, transparency and alignment with

https://orcid.org/0009-0008-0716-5082

https://orcid.org/0000-0002-4092-9522

human values is more critical than ever. The

ontological framework presented in this article also

encompasses these ethical aspects, providing a basis

for the responsible use of AI in decision-making

processes.

This article investigates the role of AI and Big

Data in enhancing evidence-based decision-making

across various sectors—including public

administration, healthcare and business intelligence.

By examining applications at both the individual and

organizational levels, our study highlights the

potential benefits and challenges of integrating these

technologies into public sector strategies.

The resulting framework is depicted as a

knowledge graph, where thematic clusters illustrate

the dynamic interconnections among technologies.

This visual representation not only clarifies complex

relationships but also aids in uncovering innovation

opportunities within decision-making processes. This

approach not only allows us to understand current

trends and challenges related to the use of AI and big

data, but also provides a framework for identifying

and developing innovative solutions that can

contribute to better decision-making at the individual,

organizational and societal levels.

Safranek, S. and Zvackova, A.

Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph.

DOI: 10.5220/0013514400003970

In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2025), pages 287-294

ISBN: 978-989-758-759-7; ISSN: 2184-2841

287

2 LITERATURE REVIEW

2.1 Analysis of Current Research in the

Field of Decision-Making Systems

Decision-making systems are defined as computer

software or information systems designed to support

managerial decision-making. The gradual evolution

of these systems has led from single manager support

to group support systems (GSS), management

information systems (MIS), Business Intelligence

(BI), knowledge management systems (KMS) and

now intelligent DSS (Mombini, 2020). This evolution

reflects the increasing complexity of decision-making

processes, requiring more advanced analytical tools

and AI-driven methodologies. As a result modern

decision-making systems integrate large-scale data

analysis, machine learning models and predictive

analytics to enhance accuracy and efficiency

While existing literature extensively discusses the

technical capabilities of DSS and AI, there remains a

gap in understanding how these systems can be

ethically aligned with human-centric values. For

instance, Dignum (2018) emphasizes the importance

of ethical AI frameworks, while Floridi et al. (2018)

advocate for AI systems that promote fairness,

accountability and transparency. This review extends

previous analyses by critically evaluating not only the

technical efficiency of decision-making models but

also their ethical implications in real-world

applications.

2.1.1 Technologies for Ontology-Based

Decision Support

Modern decision-making increasingly relies on the

integration of Big Data, Artificial Intelligence (AI)

and simulation-based models, which form the

analytical foundation of ontology-based decision

support systems.

Big Data, defined by volume, variety, velocity and

veracity (Zhang, 2018), enhances strategic decisions

by improving accuracy and enabling dynamic

adaptation (Ren, 2022). Techniques like data mining

and machine learning extract actionable insights,

particularly in customer analytics and fraud detection

(K., 2024).

Intelligent Decision Systems (IDMs) combine

simulation and neural networks to model complex

environments and improve output quality (Wang &

Dai, 2024). Transformer-based Foundational

Decision Models (FDMs) and Multi-Agent Systems

(MASs) add adaptability and distributed reasoning

(Wen et al., 2023; Cho et al., 2024).

AI acts as the cognitive layer, with deep learning

enabling advanced forecasting (Huang, 2020).

Ethical aspects such as explainability and trust are

addressed through approaches like Epistemic Quasi-

Partnership (EQP) theory (Dorsch & Moll, 2024).

Frameworks like MCDM and SDSS integrate

quantitative and qualitative inputs and have proven

effective during crises like COVID-19 (Mu, 2023;

Sidahmed & Zaraté, 2024). These technologies are

embedded in the proposed ontological framework,

structured as clusters in a knowledge graph, enabling

transparent, adaptive and ethically sound decision-

making.

2.2 Applications of AI and Big Data in

Decision-Making Systems

The integration of AI and big data into decision-

making systems has revolutionized various industries

by increasing the efficiency and accuracy of

decisions. These technologies enable organizations to

process vast amounts of data, gain valuable insights

and make informed decisions in real time. The

applications of AI and big data in decision-making

systems are diverse, examples include:

2.2.1 Business Intelligence and Predictive

Analytics

AI-driven predictive analytics are extensively used in

business to optimize operations and strategy. For

example, the Nigerian National Petroleum

Corporation integrates Big Data and machine

learning to improve forecasting and decision-making

(James et al., 2024).

2.2.2. Public Policy and Evaluation

During the COVID-19 pandemic, data-driven

systems enabled scientific policy evaluation, targeted

interventions and crisis modelling, improving

government responsiveness and legitimacy (Dong et

al., 2021; Wong, 2021). Predictive analytics

supported decisions on lockdown timing, vaccine

distribution and healthcare capacity.

2.2.3 Technological and Ethical

Considerations

Machine and deep learning are key in handling the

complexity of big data, enabling predictive

modelling, anomaly detection and recommendation

systems (SCSVMV Deemed to be University, India,

a Dr. Saraswathi M., 2024).

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

288

The ethical use of data in AI applications is

essential to ensure unbiased and fair decision-making.

This includes adhering to data ethics principles and

ensuring that AI systems are developed and used

responsibly (Rhem, 2023).

3 METHODOLOGY

The aim of this paper is to analyse the current issues

of Artificial Intelligence, Big Data and Decision-

Making in the context of its use by the general public

and also to show its benefits in the prediction of

phenomena and their and their im-pact on everyday

life. Specifically, the paper focuses on how Artificial

Intelligence and Big Data analytics can be used to

support decision-making in the public and private

sectors, not only at the individual level, but also at the

level of organizations and institutions. The aim is to

show how predictive models, based on the analysis of

huge amounts of data, can help predict various social,

economic and environmental phenomena and thus

improve the quality of decision-making.

The outputs of the analysis from the theoretical

and practical level are presented in the form of a

knowledge graph in basic clusters representing a set

of interconnected concepts and the relationships

between them. Each cluster represents a specific area

of knowledge that is connected based on their

interrelationships and interactions. The presentation

of the outputs through this graph facilitates the

understanding of the complex relationships between

technologies and processes that are necessary for the

effective use of artificial intelligence and big data in

practice. This tool not only clearly depicts the

connections between key concepts but also serves as

a foundation for further research and development in

this dynamic field.

Although we have focused on a rather superficial

level of exploration in this article, this chart provides

a useful overview of how the different technologies

and principles can be linked and what their

interdependencies are. The knowledge graph also

serves as a basis for further detailed research and

analysis that can lead to the identification of new

opportunities for improving decision-making

processes in different sectors.

This approach not only enables an understanding

of current trends and challenges related to the use of

AI and big data but also provides a framework for

identifying and developing innovative solutions that

can contribute to better decision-making at the

individual, organizational and societal level.

Literature sources and studies were selected to

create the ontological framework based on four

criteria’s - relevance of the thematic framework,

expert quality and currency, domain diversity and

ethics. The knowledge graph presented in this study

was constructed using semantic web technologies,

specifically leveraging the Resource Description

Framework (RDF) and Web Ontology Language

(OWL) to define and categorize relationships among

key concepts. RDF Data sources included peer-

reviewed journals, industry reports and real-world

case studies from sectors such as healthcare, public

policy and business intelligence. To validate the

framework, expert reviews were conducted with

professionals from both academic and industry

backgrounds, focusing on the model's applicability,

clarity and ethical robustness (Berners-Lee et al.,

2001). RDF was used to provide a basic

representation of the Subject-Predicate-Object

assertion that defines the relationships between

concepts. Based on the selected resources, a

conceptual model was created and distributed into

clusters. In the knowledge graph, each cluster

contains the key concepts identified by the literature.

The thematic clusters are designed to depict the

interactions and connections between the key areas of

this paper. For ensuring the accuracy and fluency of

the translations the text was partially processed using

DeepL – an advanced tool based on artificial

intelligence for machine translation.

4 RESULTS AND DISCUSSION

Jamarani's (2024) taxonomy of predictive analytics

applications in big data provides the foundation for a

coherent ontology model, essential in navigating the

growing volume and complexity of data. This model

enables effective integration of AI and Decision

Support Systems (DSS), improving decision accuracy

and relevance. A knowledge graph based on this

ontology visually maps key relationships—including

predictive analytics, AI, DSS and human-system

integration—facilitating intuitive exploration,

revealing patterns and supporting innovation,

research and strategic decision-making (Chen, 2020).

AI applications span multiple domains:

optimizing supply chains, enhancing e-commerce

recommendations, supporting strategic decisions,

improving healthcare diagnostics, advancing

precision agriculture and enabling smart city

planning. Through the integration of AI, Big Data and

DSS, organizations can enhance decision-making,

efficiency and adaptability.

Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph

289

Figure 1: Taxonomy of prediction analysis applications in big data (Jamarani, A., 2024).

The table below presents the key concepts that are

divided into clusters based on their role in knowledge

analysis. Each cluster represents a specific domain

that is essential for the interconnection between

different domains such as predictive analytics,

artificial intelligence, big data, decision support

systems and human systems integration. The goal is

to show how these concepts work together to support

decision-making processes in modern informatics.

Table 1: Roles of Key Concepts Across Clusters in

Knowledge Analysis.

Class Role in Analysis

Predictive Analytics Central concept linking AI, Big

Data and decision-making

rocesses.

Regression Analysis

& Classification

Combination of two main

techniques in predictive analytics,

providing the foundation for many

decision-makin

rocesses.

AI (Artificial

Intelligence)

Key element in informatics,

encompassing various techniques

to support predictive analytics and

DSS.

ML & DL (Machine

Learning & Deep

Learnin

Combination of two critical AI

technologies, enabling advanced

anal

tical ca

abilities.

Algorithmic Bias &

Ethics in AI

Critical ethical issues combined

into one node, focused on fair and

responsible AI usage.

Big Data Fundamental source for AI and

predictive analytics, essential for

model trainin

Data Quality &

Data Governance

Combination of two aspects that are

key to ensuring data is accurate and

used responsibly.

DSS Practical application of AI and

predictive analytics in real-world

decision-making processes.

Knowledge-Based

& Rule-Based

stems

Combination of two approaches in

DSS that utilize rules and

knowled

e for decision su

ort.

Transparency &

Accountability

Critical factors for trust in decision-

making processes, ensuring that

decisions are clear and

ustifiable.

Human-Systems

Integration

Ensures that technologies are usable

and use

-friendly for en

-users.

Usability Testing &

Human Factors

Combination of two key aspects

focused on system design and

usabilit

for users.

4.1 Structure and Relationships

Between Key Components of

Decision-Making Systems:

Knowledge Graph Visualization

The graph below is the result of a detailed

examination of the dependencies between predictive

analytics, artificial intelligence and decision support

systems, highlighting the importance of including

human-system integration. It consists of five

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Figure 2: Knowledge Graph of Predictive Analytics and AI in Decision-Making.

interconnected clusters representing key domains of

modern informatics and technology.

The Big Data Cluster focuses on foundational

concepts of data processing for modern decision-

making. Technologies such as IoT and Cloud

Computing support data acquisition and

management, with Data Processing and Data

Visualization playing essential roles. While Cloud

Computing and Data Storage are related, the former

includes broader services such as application

management.

The Predictive Analytics Cluster includes

techniques like regression, time series analysis and

anomaly detection to anticipate trends and risks.

Predictive analytics uses historical data, algorithms

and machine learning to build models that support

informed decision-making (McCarthy, 2022). These

principles are captured within the knowledge graph.

Artificial Intelligence Cluster are concepts like

Data Mining bridge AI and Big Data. Data Mining

uses AI techniques (e.g., neural networks) to identify

useful patterns in large datasets (Kantardzic, 2011;

Han, 2022). The cluster also includes Algorithm Bias

and Ethical AI, emphasizing fairness and responsible

AI practices.

Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph

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The Human-System Integration Cluster focuses

on the interaction between users and technologies,

including User Experience (UX), Ergonomics and

interface design. It draws from psychology,

engineering and design to ensure that systems are

intuitive, safe and efficient.

The Decision Support System Cluster includes

Knowledge-Based Systems, Rule-Based Systems and

Decision-Making processes, all linked with ethical

principles such as fairness, transparency, privacy and

accountability.

5 ETHICAL AND SOCIETAL

IMPLICATIONS OF AI AND

BIG DATA IN DECISION-

MAKING SYSTEMS

As AI-driven systems become increasingly

autonomous, ethical considerations such as

algorithmic bias, data privacy and decision

transparency become paramount. These systems

influence critical areas such as criminal justice,

healthcare, finance and public policy, where biased or

opaque decisions can have severe societal

consequences. Algorithmic bias occurs when AI

models produce systematically prejudiced outcomes

due to biased data or flawed model design. For

example, the COMPAS algorithm used in the U.S.

judicial system has faced criticism for racial bias in

recidivism predictions, disproportionately affecting

minority groups (Angwin et al., 2016). Addressing

such biases requires diverse datasets, transparent

algorithm design and continuous model evaluation.

With the proliferation of Big Data, concerns about

data privacy and security have intensified. AI systems

rely on vast datasets, often containing sensitive

personal information. Frameworks like the General

Data Protection Regulation (GDPR) emphasize data

minimization, user consent and transparency to

safeguard individuals' privacy. Explain ability in AI

refers to the ability to understand and interpret how

decisions are made. Black-box models, particularly in

deep learning, pose challenges in this regard.

Implementing explainable AI (XAI) techniques

enhances accountability and fosters trust among users

and stakeholders (Floridi et al., 2018). The European

Union's AI Act (European Commission, 2021) and

IEEE’s guidelines on ethically aligned design provide

frameworks for responsible AI development. These

guidelines promote human oversight, accountability

and ethical risk assessments throughout the AI

lifecycle. Moreover, AI systems can exacerbate social

inequalities if not designed with inclusivity in mind.

Ethical AI development should prioritize human

well-being, ensuring technologies enhance societal

benefits without marginalizing vulnerable

populations (Dignum, 2018).

6 FUTURE RESEARCH

DIRECTIONS

Future research in the field of Artificial Intelligence

(AI), Big Data and Decision Support Systems (DSS)

should focus on several key areas in light of rapid

technological advancements. The integration of

emerging technologies such as quantum computing

and blockchain holds the potential to revolutionize

data security, processing speed and decision-making

accuracy (Tang, 2024). A critical direction is the

development of Explainable AI (XAI), which

enhances the transparency of decision-making

processes and fosters user trust (Das, Zhang, &

Kiszka, 2024). Further research should investigate

adaptive algorithms capable of continuous learning in

real-time without explicit human intervention

(Elhaddad & Hamam, 2024). Understanding the

impact of AI and DSS in different cultural and

organizational contexts through comparative studies

is also essential, as it can reveal how socio-economic

factors influence the effectiveness of these systems

(Ramachandran et al., 2023). Additionally,

interdisciplinary collaboration is crucial, fostering

comprehensive approaches to the implementation of

AI and DSS across sectors such as healthcare, public

policy and education (Sharma et al., 2023). The future

of AI, Big Data and DSS thus lies in the development

of systems that are not only technologically advanced

but also ethically sustainable, adaptable and inclusive

across diverse domains and cultures.

7 CONCLUSION

The integration of Artificial Intelligence (AI), Big

Data and Decision Support Systems (DSS) presents

transformative opportunities for enhancing decision-

making across various domains. This paper has

introduced an ontological framework based on

knowledge graphs, providing a structured

representation of the complex interrelationships

between predictive analytics, AI, DSS and human-

system integration. The framework facilitates not

only more informed and efficient decision-making

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but also emphasizes ethical considerations,

transparency and accountability in AI applications.

Our findings highlight the critical role of

predictive analytics in anticipating trends and risks,

the importance of ethical AI to ensure fairness and

reduce algorithmic biases and the value of human-

system integration for user-centric technology design.

By visualizing these interconnections, the knowledge

graph aids in identifying patterns, fostering

innovative solutions and supporting data-driven

strategies.

The proposed framework has practical

implications for sectors such as healthcare,

finance,and public policy-enhancing patient risk

assessment, improving fraud detection and enabling

real-time social trend monitoring. By supporting

structured data integration and analysis, it helps

organizations improve decision accuracy,

efficiency,and ethical compliance.

Looking ahead, future research should focus on

advancing explainable AI, adaptive algorithms and

interdisciplinary collaborations to address emerging

challenges. The ontological framework presented

here offers a foundation for further exploration,

ensuring that the evolution of AI and Big Data

technologies contributes to responsible, effective and

sustainable decision-making at individual,

organizational and societal levels.

ACKNOWLEDGMENT

The research has been partially supported by the

Faculty of Informatics and Management UHK

specific research project 2108 Addressing Current

Challenges in the Field of Smart Cities and

Cybersecurity.

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