Semantic Computing Lab

Enabling Semantic AI, Interoperability, and Cognition in Digital Manufacturing Systems and Supply Chains

The Semantic Computing Lab at Arizona State University focuses on conducting research in explicit knowledge representation and reasoning (KRR) and ontology engineering in support of knowledge-intensive and cyber-enabled design and manufacturing and digital supply chains. KRR is the brain behind automated intelligent decision-making. It’s the secret that allows machines to understand, interpret, and apply knowledge—just like we humans do!

We develop formal ontologies and semantic knowledge graphs (KG) together with tools and methods for formal knowledge acquisition, representations, and synthesis with the objective of improving the intelligence and interoperability in design and manufacturing systems and digital supply networks.

We call ourselves Knowledge Engineers. Knowledge engineering is a branch of artificial intelligence (AI) that is concerned with building knowledge-based systems (KBS). One of the core focuses of researchers at Semantic Computing Lab is Neurosymbolic AI with applications in manufacturing systems and networks. Neurosymbolic AI is a field of artificial intelligence that combines elements of both symbolic AI and deep learning approaches. It aims to integrate the strengths of semantic reasoning and and machine learning- the two main strands of AI- to create more powerful and comprehensive AI systems.

From a methodological standpoint, we work in the following areas:

• Ontology Engineering
• Knowledge Representation and Reasoning (KRR)
• Semantic Knowledge Graphs (SKG) and Graph-native Machine Learning (GN-ML)
• Neurosymbolic AI
• Graph Neural Network (GNN)
• Machine Learning (ML) and Artificial Intelligence (AI)
• Natural Language Processing (NLP)
• Robust Optimization
• Systems Engineering
• Decision Theory
• Design Theory and Methodology
• Information Theory

From an applicability standpoint, we work in the following areas:

• Digital Supply Networks
• Smart Manufacturing/Industry 4.0
• Manufacturing Diagnostics & Prognostics
• Industrial Maintenance
• Manufacturing Sustainability and Circular Economy (CE)
• Design and Manufacturing Automation
• Cyberphysical Manufacturing Systems
• Product Design and Development
• Design for Manufacturing (DFM)
• Traceability in Agri-food Supply Chains

Research Objectives:

The major objectives of the Semantic Computing Lab are:

• Enabling automated intelligent decision-making in distributed design, manufacturing, and logistics through developing decision support tools and semantic information models.
• Enabling distributed and cyber-enabled design and manufacturing through developing tools, methods, and systems for horizontal collaboration.
• Enabling autonomous design-to-fabrication in automated manufacturing systems through providing manufacturing systems and assets with cognitive capabilities
• Improving the performance of machine learning techniques through semantic enrichment, integration, and harmonization of data.

MSN

The School of Manufacturing Systems and Networks is a new school for a new time. The transdisciplinary nature of the school’s curriculum, research portfolio, and industry engagements is preparing students to be leaders of the processes and systems that will drive the future of manufacturing.

Contact:

Dr. Farhad Ameri

Technology Center Suite 100

6075 S. Innovation Way West

Mesa, AZ 85212