# Presentation: 924650

## 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

## 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.

## 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.

## 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
- ...

## Situated Cognitive Engineering

- Operational
- Demands

- Human Factors
- Knowledge

- Envisioned
- Technology

- Requirements Baseline

- Scenarios

- Claims

- Core Functions

- Prototype

- Evaluate

- Refine

- Review

- Current & Simulated Technology

- User Experience

- Comments

## Hyperthermic  Astronaut Scenario

## MECA 2017 Architecture

## 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.

## 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

## 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

## 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

## Semantic Languages: the W3C stack

- Image: W3C

- 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

## The MECA Entity Hierarchy

## Object Ontologies

- Ecosystems
- space, planet, etc.
- Landscape and terrain features
- Natural resources
- Weather
- Geospatial information
- Temporal information

## 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...

## 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

## MECA 2017 Architecture

## 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

## 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

## Final conclusion

- Ontologies/OWL/RDF
- Not a silver bullet
- Not a baseless hype, either

## Thank you for listening!

|  | Photo: ESA/Aurora |
| --- | --- |