Apps

Description:

Ontologies itself cannot provide search functionalities and data evaluation. The Semantic Information Management System (SIMS) provides a unified environment for managing complex technical and engineering data. It transforms heterogeneous datasets into a semantic knowledge graph, enabling intelligent querying, reasoning, and decision-making.

Designed as a modular architecture, each layer of SIMS can operate independently, yet communicate seamlessly via REST APIs or through shared databases. This flexibility allows project teams to adopt only the modules they need, while ensuring scalability and interoperability across domains.

In today’s engineering and research environments, data is highly fragmented and spread across formats, systems, and isolated units. Traditional approaches often fail to unlock the full value of this information.

SIMS carries out the following:

• unifying scattered data into a coherent, semantically enriched knowledge base
• enabling interoperability between tools, standards, and partners
• empowering decision-makers with advanced reasoning, semantic search, and AI-driven insights

By bridging the gap between raw data and actionable knowledge, SIMS becomes a strategic enabler for digital transformation, supporting innovation, compliance, and competitiveness across industries.

The Semantic Information Management System is built on four key concepts: taxonomy, ontology, knowledge graph, and semantics.

• Taxonomy: A simple classification system that organizes concepts into a hierarchical structure (e.g., parent-child classes) for systematic classification.
• Ontology: A formal knowledge model that goes beyond simple classification by explicitly defining concepts, their attributes, and the logical relations among them. E.g., “the battery has capacity 120 kWh” or “the WAAM process consumes feedstock”.
• Knowledge Graph: A graph-structured representation of knowledge that integrates ontologies together with real-world data. It enables systems to answer complex questions, perform reasoning, and uncover hidden insights.
• Semantics: Explicit representation of meaning in data and models. It ensures that terms, properties, and relationships are interpreted consistently by humans and machines. The adjective “semantic” is often used in compound terms such as semantic information, semantic web, or semantic interoperability.

Use Case:

The web-based tools to query the data and ontology can be run in cloud or locally on a system.

Additional Information

Link