Semantic Intelligence in Big Data Applications

TL;DR

This paper discusses how semantic intelligence enhances big data applications by enabling machines to understand and process complex, heterogeneous data in enterprise and IoT systems, facilitating smarter, context-aware decision-making.

Contribution

It provides a comprehensive overview of semantic intelligence technologies, tools, and challenges in large-scale distributed and industrial data systems.

Findings

Semantic intelligence bridges the gap between human and machine understanding.

Tools leveraging semantics improve data integration and processing at scale.

Challenges include heterogeneity, interoperability, and explainability in AI systems.

Abstract

Today, data is growing at a tremendous rate and, according to the International Data Corporation, it is expected to reach 175 zettabytes by 2025. The International Data Corporation also forecasts that more than 150B devices will be connected across the globe by 2025, most of which will be creating data in real-time, while 90 zettabytes of data will be created by the Internet of Things devices. This vast amount of data creates several new opportunities for modern enterprises, especially for analysing the enterprise value chains in a broader sense. To leverage the potential of real data and build smart applications on top of sensory data, IoT-based systems integrate domain knowledge and context-relevant information. Semantic Intelligence is the process of bridging the semantic gap between human and computer comprehension by teaching a machine to think in terms of object-oriented concepts in the same way as a human does. Semantic intelligence technologies are the most important component in developing artificially intelligent knowledge-based systems since they assist machines in contextually and intelligently integrating and processing resources. This Chapter aims at demystifying semantic intelligence in distributed, enterprise and web-based information systems. It also discusses prominent tools that leverage semantics, handle large data at scale and address challenges (e.g. heterogeneity, interoperability, machine learning explainability) in different industrial applications.