KM - KNOWLEDGE Manager
A SMARTer way to digitalize your knowledge
Knowledge Management
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 person at the appropriate time.
KM – KNOWLEDGE Manager 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.
Quality
The more knowledge you explicit in a Knowledge base, the more advanced engineering activities can be performed (automatic verification of workproducts, semantic search engines along a project, semantic suggestion of traces…).
Time
KM – KNOWLEDGE Manager eases knowledge sharing and reuse activities among different engineering tools, allowing users to evolve and update ontologies seamlessly.
Money
Proper knowledge management is an asset for the organization that translates into earnings and savings. Knowledge is Money!
Features
Authoritative Source of Truth
KM – KNOWLEDGE Manager permits the creation of a specific ontology to address the full complexity of the project’s context and tackle any kind of semantic structure required.
A controlled vocabulary is a must to facilitate consistency across the different work products developed during the life cycle of a project.
Ontologies in KM – KNOWLEDGE Manager help specific relationships between terms to fully represent a project’s context: synonyms, parent-child dependencies, subsystems, functional structures, etc.
Patterns
The feature of creating textual patterns is a flexible solution to satisfy personalized preferences while writing requirements or other kind of textual workproducts (risks, tests scenarios…). It helps 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 Manager enables interfacing with several external sources so that the ontology includes multiple sources of truth.
Managing Knowledge Repositories
KM – KNOWLEDGE Manager 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 KM – KNOWLEDGE Manager for quality analysis, requirements, and textual work product authoring, to identify different types of link traces, transforming from requirements to models or test cases, identification of reusable products, etc.
KM – KNOWLEDGE Manager enables the management of the System Knowledge Repository, its System Knowledge Base, and all assets involved in the life cycle of your systems.
KM and Systems Engineering
KM – KNOWLEDGE Manager 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 make it available to subsequent projects.
The structure of our knowledge bases includes 5 different layers:
1. Fill your ontology with the domain-specific controlled vocabulary on top of the already included generic vocabulary.
2. Build hierarchical structures and create relationships between the terms of your controlled vocabulary as a way to classify this vocabulary. Grouping concepts together within our semantic clusters is the second mechanism to provide more semantics on top of the vocabulary.
3. Design the textual structures, and patterns, that you can reuse while writing the requirements or other types of textual workproducts. Such patterns can be also used in plenty of other semantic activities such as the identification of properties from textual sources, the suggestion of traces…
4. Once a pattern is “matched” for a given textual input, this input can be Formalized (transformed) into one or several semantic graphs. Those semantic graphs are the right input for the reasoning layer performed by the system.
5. The reasoning layer represent a complete toolbox that enables semantic operation to be performed with your workproducts such as the automatic verification of the quality of your items, the suggestion of traces, the semantic search engine…
Textual patterns: standardize and rewrite requirements
Textual Patterns represent the grammatical structure that a natural language sentence needs to follow according to an organization’s policies and know-how. Textual patterns can also be used to enable text transformation. By setting up source and target patterns for a transformation, the KM – KNOWLEDGE Manager will detect requirements that match the pattern to execute the transformation to the target structure while maintaining the semantic consistency.
Semantic indexing and retrieval
Using Natural Language Processing tools and Artificial Intelligence algorithms, KM – KNOWLEDGE Manager provides a semantic search engine that enables the search and reuse of all sorts of information based on its actual meaning.
Libraries
KM – KNOWLEDGE Manager provides the capability to use Knowledge Libraries: combinations of Knowledge items of different natures and levels of abstraction that can be reused in numerous projects. Knowledge management based on Libraries is the best way to blend knowledge flexibly. 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 to Writing Requirements.
- EARS Knowledge Library: includes the catalog 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 European Cooperation for Space Standardization.
- Requirements with Numbers Library: Including 17 metrics, dealing with the numbers used in textual requirements.
- BABOK® Library: Including a set of Quality Metrics covering the subsequent RADD characteristics and a set of patterns applied to cover the nine RADD characteristics.
External Connectors
External connectors allow for merge, in real-time, between the knowledge natively managed in KNOWLEDGE Manager (mainly corresponding to layers 1 and 2 of the Knowledge Base), and information coming from one or more external sources. Thus, if part of your knowledge is managed in MBSE tools, external ontologies in Protégé, or even MS Excel or Visio, there is no need to export and import in KM, you can just add an external connector from KM – KNOWLEDGE Manager, later, when users make use of the Knowledge Base from other tools of the suite such as SES ENGINEEERING Studio, RAT AUTHORING Tools…, the information from such external source shall be blended in real-time with the information from the KM ontology.