Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Fraunhofer Institute for Algorithms and Scientific Computing SCAI

Semantic Information Management System (SIMS)

Software Solution and Customization Services

A modular platform for intelligent data integration and knowledge management

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.

The SIMS addresses this challenge by:

  • 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.

Background

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.

Features

Data Ingestion & Processing Layer

 

  • Planned support for structured (CSV, JSON, XML), semi-structured (VMAP), and unstructured (PDF, images) data.
  • The software cleans, normalizes, and validates datasets to ensure consistency and reliability.
  • Automated metadata annotation and schema alignment will prepare data for semantic enrichment.

Benefit: Reliable and standardized input for advanced applications.

Semantic Modelling

 

  • Taxonomy Engine will define hierarchical classifications.
  • Ontology Engine will encode relationships, rules, and constraints.
  • Integration of external standards (OWL, RDF, JSON-LD) to ensure interoperability.

Benefit: Provides semantic coherence, enabling datasets to become machine-interpretable knowledge.

Knowledge Graph

 

  • Transform semantic models into a connected, queryable knowledge graph.
  • The Graph Creator Engine will build relationships, while the Validation Engine ensures logical consistency.
  • Accessible through SPARQL and graph APIs for advanced reasoning and search.

Benefit: Discover insights and relationships hidden in fragmented data.

Web Application Layer

 

  • Role-based user access with OAuth2 authentication.
  • Graph Viewer will offer intuitive graph exploration, visualization, and search.
  • Support for integration of conversational AI for natural language queries will be provided.

Benefit: Secure, interactive, and user-friendly interface for experts and non-experts alike.

Distributed Storage

 

  • Polyglot persistence strategy to be used
  • Data will be stored on-premises to ensure compliance, control, and security.

Benefit: Flexible and secure storage tailored to diverse data types.

Security by Design

 

SIMS will embed security practices across all layers:

  • SAST, DAST, SCA to ensure code and dependencies remain secure.
  • Encryption and policy-based access controls for sensitive metadata.
  • GDPR alignment for user data and session handling.