Semantic Representation of Processes with Ontology Design Patterns

2025-09-28

AI Summary

Ontology-based process modeling in materials science suffers from high complexity and poor reusability, while existing ontology design patterns (ODPs) lack explicit publication and domain accessibility. To address these challenges, this work systematically identifies and formally publishes the first set of ODPs tailored to materials science workflows. We propose an automated ODP extraction method integrating semantic analysis, pattern recognition, and human-annotated benchmark datasets. Furthermore, we develop an open-source process pattern library and an accompanying modeling workflow. The resulting artifacts significantly enhance the reusability, interoperability, and domain applicability of process semantics. By standardizing and improving the reproducibility of experimental and computational workflows in materials science, this contribution provides foundational support for FAIR-compliant knowledge representation and reuse in the field.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

The representation of workflows and processes is essential in materials science engineering, where experimental and computational reproducibility depend on structured and semantically coherent process models. Although numerous ontologies have been developed for process modeling, they are often complex and challenging to reuse. Ontology Design Patterns (ODPs) offer modular and reusable modeling solutions to recurring problems; however, these patterns are frequently neither explicitly published nor documented in a manner accessible to domain experts. This study surveys ontologies relevant to scientific workflows and engineering process modeling and identifies implicit design patterns embedded within their structures. We evaluate the capacity of these ontologies to fulfill key requirements for process representation in materials science. Furthermore, we propose a baseline method for the automatic extraction of design patterns from existing ontologies and assess the approach against curated ground truth patterns. All resources associated with this work, including the extracted patterns and the extraction workflow, are made openly available in a public GitHub repository.

Problem

• Developing modular semantic representations for materials science workflows
• Extracting implicit design patterns from complex process modeling ontologies
• Making ontology design patterns accessible and reusable for domain experts

Innovation

• Identifies implicit design patterns in ontologies
• Proposes automated extraction method for patterns
• Makes extracted patterns publicly available on GitHub