Enabling SMART Systems Engineering

KM - KNOWLEDGE Management


The SMARTer way to digitalize your knowledge

KM – Knowledge Manager

KM - KNOWLEDGE Management

KM – KNOWLEDGE Management is a capability of The SES ENGINEERING Studio, a software environment aimed to digitalize systems engineering capabilities.

Knowledge is one of the most valuable assets in your organization. The key driver to success in any system or software project is to reuse knowledge assets. These include engineers’ explicit and tacit knowledge, and guidelines defining the organizational know-how.

Knowledge should therefore be gathered from different sources, stored in secure repositories, and accessed by the appointed personnel at the appropriate time.

KM – KNOWLEDGE Management allows you to manage knowledge from the systems engineering point of view and to store valuable information from requirements, models, system architectures and other documents in a common System Knowledge Base.

Knowledge Manager


The more knowledge you manage in KM – KNOWLEDGE Management, the more advanced analysis can be performed by the SES ENGINEERING Studio different Capability: RQA – QUALITY Management, V&V, RAT – AUTHORING Tool and TRACEABILITY Management, etc.


KM eases knowledge sharing and reuse activities among different engineering tools, allowing users to evolve and update Ontologies seamlessly.


Proper knowledge management is an asset for the organization that translates into earnings and savings.


 Unique ontology

KM – KNOWLEDGE Management permits to create a specific ontology in order to address the full complexity of the project’s context and tackle any kind of semantic structure required.

A controlled vocabulary is a must in order to facilitate consistency across the different work products developed during the lifecycle of a project.

Ontologies in KM help establishing specific relationships between terms in order to fully represent a project’s context: synonyms, parent-child dependencies, subsystems, functional structures, etc.


The feature of creating patterns is a flexible solution to satisfy the personalized preferences while writing the requirements. It helps to optimize the editing process, standardize the writing approach and englobe any possible variations within the requirements’ specifications.

Interface with external sources

As your project knowledge can be stored in several different formats (e.g. SysML/UML models, simulations, tables, external databases, …) KM – KNOWLEDGE Management enables interfacing with several external sources so that the ontology includes multiple sources of truth.

Managing knowledge repositories

KM – KNOWLEDGE Management is designed to manage all the knowledge needed for your system or software intensive projects (breakdown structures, terms, acronyms, restrictions, etc.). Knowledge is stored within a System Knowledge Repository (SKR) and is organized in ontologies (called System Knowledge Base – SKB) and knowledge libraries. The Ontology and the libraries are used by the SES ENGINEERING Studio for quality analysis, Requirements and textual work products authoring, to identify different types of link traces, to transform from requirements to models or test cases, identification of reusable products, etc.

KM – KNOWLEDGE Management enables the management of the System Knowledge Repository, its System Knowledge Base, and all assets involved in the lifecycle of your systems.

KM and Systems Engineering

KM – KNOWLEDGE Management is the core tool for the Knowledge Centric Systems Engineering approach which aims to take advantage of all the knowledge developed during the System definition phase and thus making it available to subsequent projects.

Knowledge library

An example of this Knowledge structure is represented in the following figure.

KM perfect Knowledge structure

Textual patterns

Textual Patterns represent the grammatical structure that a natural language sentence needs to follow according to an organization’s policies and know-how. The different packages conforming the SES ENGINEERING Studio apply patterns to state which guidelines should apply to the project’s requirements, risks, etc. either for writing new statements or to assess their quality. For instance, the requirement “When switched on, the Cab radio should be applied within a temperature range of -20C to +70C”, matches the following pattern in KM – KNOWLEDGE Management:

KM sentence patterns

Semantic indexing and retrieval

Using Natural Language Processing tools and Artificial Intelligence algorithms, KM provides a semantic search engine that enables the search and reuse of all sort of information based on its actual meaning.

KM semantic indexing


KM – KNOWLEDGE Management provides the capability to use Knowledge Libraries: combinations of Knowledge items of different nature and levels of abstraction that can be reused in numerous projects. Knowledge management based on Libraries is the best way to blend knowledge in a flexible way. The REUSE Company provides a wide catalogue of Libraries ready to plug and play. Our current set of libraries is:

  • INCOSE Knowledge Library: focuses on metrics defined as Quality rules in the INCOSE Guide for Writing Requirements.
  • EARS Knowledge Library: includes the catalogue of patterns as defined in the Easy Approach to Requirements Syntax.
  • SOPHIST Master Patterns Library: includes all the patterns described by SOPHIST in its Master Catalogue plus 18 rules for Requirements Writing.
  • NASA Knowledge Library: includes the NASA Glossary plus the rules for writing requirements as described in the NASA Systems Engineering Handbook.
  • ECSS Knowledge Library: includes terms, patterns and drafting rules defined by the the European Cooperation for Space Standardization.


License Types

KM License Type
KM Lite
KM Full
Terminology layer:
- Terminology
- Term tags
- Languages
- Tokenization rules
- Normalization
- Disambiguation
- Advanced Import/export
Conceptual Model layer:
- Hierarchical views
- Semantic clusters
- Relationship taxonomy
- Suggestions
- Dashboards
- Advanced Import/export
Patterns layer:
- Sentences pattern
- Complex patterns
- Pattern groups
Formalization layer:
- Relationships
- Properties
Inference layer:
-Rule-Based Inference
Configuration management layer:
- Configuration management
- Changes federation
Extensibility layer:
- Import Libraries
- Generate Library
- Knowledge interfaces


Assets store layer:
- Artifacts
- Artifact types
- Artifact indexing
Settings layer:
- Users
- Active Directory integration
- Indexing
- Retrieval
Ontology Copy:
- Partial Copy
- Complete Copy