The MECA Project Using an OWL/RDF Knowledge Base to ensure Data Portability for Space Missions

1. The MECA Project Using an OWL/RDF Knowledge Base to ensure Data Portability for Space Missions Mark Neerincx, Jasper Lindenberg, Nanja Smets, Tim Grant, André Bos, Leo Breebaart, Antonio Olmedo Soler, Uwe Brauer, Mikael Wolff

2. The MECA Consortium TNO Human Factors • Human behaviour and performance in technical high-demand environments; methods to attune the environment to (momentary) human capacities. Science & Technology BV • Software development company with specific expertise on creating system health management applications. OK Systems • Software development company focusing on technology areas of AI, user interfaces, databases and web-based systems (specially scheduling systems). Astrium-ST • Technologies, development, production and utilization of manned and unmanned space missions, including experiments, space transportation systems, propulsion systems and support of these systems concerning operations, maintenance and mission handling.

3. Objective & Vision Objective: support mission goals (without injury or loss of life) by • empowering the cognitive capacities of human-machine teams during planetary exploration missions • in order to cope autonomously with unexpected, complex and potentially hazardous situations. Vision: crew support that • acts in a ubiquitous computing environment • as “electronic partner”, helping the crew • to assess the situation, • to determine a suitable course of actions to solve a problem, • to safeguard the astronaut from failures.

4. The MECA Research Project • Phase 1 (2005-2006) • “MECA 2017” • Theoretical technology review • Use Cases • RB: Requirements Baseline • Phase 2 (2006-2007) • “MECA 2007” • Proof of Concept Demonstrator (PoC) • Human-in-the-loop evaluation • Refined RB • Phase 3 (2008- …) • MARS 500 • New 4-year project • …

5. Situated Cognitive Engineering Operational Demands Human Factors Knowledge Envisioned Technology Scenarios Claims Core Functions Requirements Baseline Current & Simulated Technology Prototype Review Evaluate Refine Comments User Experience

6. Hyperthermic Astronaut Scenario

7. MECA 2017 Architecture

8. What are ontologies? “Ontologies are an explicit specification of a conceptualization” —Tom Gruber • An ontology specifies the concepts, relationships, and other distinctions that are relevant for modeling a domain. • The specification takes the form of the definitions of classes, relations, etc, which provide formal meanings (semantics) for the vocabulary.

9. Ontology advantages • Data portability • “Open world” assumption • Co-existing data (and meta-data) in uniform representation • Semantics become data: moved out of documentation and out of application code • Formal Specification • Automated reasoning, applying rules, etc. • Automated validation of instance data • Generation of documentation, templates, code • Part of the “semantic web” standards

10. Why Ontologies in MECA 2017? • MECA Ontology requested as project deliverable by ESA • Future reusability of project results • Integration of systems and data products from different parties, and at different levels of abstraction, through ontology descriptions of data and metadata

11. Why Ontologies in MECA 2007? • Loosely coupled Service-Oriented-Architecture (SOA) recommended for MECA • Demonstrator as vehicle for experimentation • Decision: Use RDF/OWL as Knowledge Base implementation

12. Semantic Languages: the W3C stack • RDF (Resource Description Format) • Assertions about things • No semantics beyond that • Graph-based data format for objects and relationships • RDFS: • Defines the concepts of Classes and Properties • OWL (Ontology Web Language) • Vocabulary for describing restrictions on and relations between Classes and Properties Image: W3C

13. The MECA Entity Hierarchy

14. Object Ontologies • Ecosystems • space, planet, etc. • Landscape and terrain features • Natural resources • Weather • Geospatial information • Temporal information

15. Actor Ontologies • Hardware Systems • Vehicles • Payloads • Suits • Robots • Sensors, actuators, processors, telemetry • Software Systems • MECA Units • Simulation modules • Services • Organic Systems • Astronauts • Other humans • Pets • Aliens…

16. Concept Ontologies • TasksMissions, Objectives (=Goals), Experiments, Procedures, Activities, Plans, Schedules, Assignments, Timelines, Timetables, Situations (Scenarios), Events • CommunicationsMessages, Alerts, Priorities, Contexts, Channels, Interfaces, Event Logs, Data archives • System HealthTests, Problems (Malfunctions, Failures, Diseases, Mistakes), Symptoms, Diagnostics, solutions (remedies, therapies), Contingency plans, Rules • Human InterfaceCognitive processes, task load, emotion, user interfaces, use case analysis

17. MECA 2017 Architecture

18. Experiences with RDF/OWL: the Bad • Fairly steep learning curve • Immaturity of tools • Lack of programmatic support • Modeling is not trivial • Performance / bloat • No chance to experiment with rules

19. Experiences with RDF/OWL: the Good • Ontology approach successful in MECA 2007 • Tools are solid • Rapid prototyping, data portability, separation of concerns • SPARQL query language is very promising • Clear potential for smooth real-time operational knowledge sharing between humans and autonomous systems on planetary missions

20. Final conclusion • Ontologies/OWL/RDF • Not a silver bullet • Not a baseless hype, either

21. Thank you for listening!