Temporal Ontology Learning: Artificial Intelligence That Discovers New Dimensions of Time

Mahabu Subhani Mohammed

Abstract: The combination of artificial intelligence (AI) and knowledge representation, and more particularly temporal reasoning, creates an integration which is a monumental shift toward systems that can conceptualize and understand time in new ways. In this paper, we discuss the emerging area of temporal ontology learning which concerns the automated acquisition and incremental formalization of conceptual models with explicit temporal semantics. We construct the foundational terms, strategies, and various uses of this interdisciplinary field by integrating contemporary works in AI, machine learning, natural language processing and ontology engineering. We discuss how AI methods aid in the continuous modeling of arbitrary points in time, the discovery of concealed temporal structures, and the representation of intricate recurring time patterns. We present difficulties with expressiveness, the incompleteness of data, and the complexities of evaluation, all with the scope of future advancements. This synthesis illustrates how important temporal ontology learning is for the advancement of intelligent systems that need to understand, reason, and adapt to the nature of the relationships and events in different science and technology domains.

Keywords: Temporal Ontology, Temporal Knowledge Graph, Representation Learning, Temporal Reasoning, Deep Learning for Time-Series, Spatio-Temporal Modeling, Knowledge Tracing, Explainable AI

How to Cite?: Mahabu Subhani Mohammed, "Temporal Ontology Learning: Artificial Intelligence That Discovers New Dimensions of Time", Volume 14 Issue 9, September 2025, International Journal of Science and Research (IJSR), Pages: 1252-1257, https://www.ijsr.net/getabstract.php?paperid=SR25925164050, DOI: https://dx.doi.org/10.21275/SR25925164050