=Paper=
{{Paper
|id=Vol-2969/paper37-FoisShowCase
|storemode=property
|title=The BAALL Ontology - Configuration of Service Robots, Food, and Diet
|pdfUrl=https://ceur-ws.org/Vol-2969/paper37-FoisShowCase.pdf
|volume=Vol-2969
|authors=Bernd Krieg-Brückner,Serge Autexier,Mihai Pomarlan
|dblpUrl=https://dblp.org/rec/conf/jowo/Krieg-BrucknerA21
}}
==The BAALL Ontology - Configuration of Service Robots, Food, and Diet==
https://ceur-ws.org/Vol-2969/paper37-FoisShowCase.pdf
The BAALL Ontology –
Configuration of Service Robots, Food, and Diet
Bernd Krieg-Brückner1,2 , Serge Autexier1 and Mihai Pomarlan2
1
German Research Center for Artificial Intelligence, DFKI, BAALL, Bremen, Germany
2
Collaborative Research Center EASE, Universität Bremen, Germany
Abstract
The BAALL Ontology, originally motivated by Ambient Assisted Living, now comprises more than 40k
OWL axioms to integrate diverse applications, covering a foundational, a variety of general, and several
application domain ontologies for configuration of service robots, diets, structured food products and
dishes, and cooking assistance. To maintain structural consistency, safe ontology extension is supported
by Generic Ontology Design Patterns.
Keywords
service robots, food, diet, generic ontology design patterns, qualitatively graded relations
1. Introduction
The BAALL Ontology has been developed at DFKI’s Bremen Ambient Assisted Living Lab, BAALL,
the original work on configuration of mobility assistants (cf. Sect. 3) being motivated by AAL.
Its domain has since been extended to cover configuration of diets (cf. Sect. 4) and robotics,
food products and dishes (cf. Sect. 5), cooking assistance (cf. Sect. 6), and support for service
robots (cf. Sect. 7)1 . It now comprises more than 40k OWL axioms; thus structuring (cf. Sect. 2)
and safe extension (cf. Sect. 8) are essential.
2. Structure, Foundation
DUL and DULsineA. Consider Fig. 1: the BAALL “hyper-ontology” is modularised into
separate ontologies with import relations. Via the module Foundation, compatibility with DUL
is maintained. DUL, DOLCE Ultra Light (see www.ontologydesignpatterns.org/ont/dul/) is
based on the “Upper Ontology” DOLCE, providing universal concepts as a common ground.
DUL is not used directly, but abstracted to DULsineA (“without axioms”), a version of DUL
stripped of most relations and axioms, but keeping the essential structuring categories. New
FOIS 2021 Ontology Showcase, held at FOIS 2021 - 12th International Conference on Formal Ontology in Information
Systems, September 13-17, 2021, Bolzano, Italy
" Bernd.Krieg-Brueckner@dfki.de, bkb@uni-bremen.de (B. Krieg-Brückner); Serge.Autexier@dfki.de
(S. Autexier); Mihai.Pomarlan@uni-bremen.de (M. Pomarlan)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
Partial support has come from the German Research Foundation (DFG), as part of the Collaborative Research
Center 1320 “EASE - Everyday Activity Science and Engineering” (http://www.ease-crc.org/)
DULSINEA
FOUNDATION DUL
• Universal Concepts (DOLCE Ultra Light)
• “Upper Ontology” without Axioms
MEDICINE BIOLOGY CHEMISTRY PHYSICS OBJECT
• Disease • Eukaryota • ChemicalCompound • PhysicalQuantity • PhysicalAspect
• RestrictedDiet • hasIngredient
• comprises...
FLAVOUR ECONOMY GEOGRAPHY ARTIFACT
• FlavourCompound • Price • GeoRegion • PhysicalContainer
• CookWare
PERSON
• hasDisease
• isAllowedDish
FOD_SOURCE
• fromEukaryota FOD_INGREDIENT
• hasFlavour- • hasSource FOD_PRODUCT
Compound • hasFODIngredient DISH
• hasLowValue
• hasHighValue
DIET
• DietRestriction
• allowsFOD
• allowsDish SHOPPING COOKING
• ShoppingList • RecipeTask
• processFOD
Figure 1: Structure of the BAALL Ontology, with import relations
categories, characteristic relations, and axioms are only introduced by Generic Ontology Design
Patterns when required, cf. Sect. 8.
3. Configuration of Mobility Assistants
The BAALL Ontology was motivated by the project Assistants for Safe Mobility (ASSAM, www.
assam-project.eu) [1], where multifaceted variants of smart mobility assistants have been
developed on the basis of wheelchairs and walkers. These are intended to compensate for
individual age-related impairments: end-users may be afflicted by diseases leading to motoric
impairments (e.g. loss of endurance, visibility) or cognitive impairments (e.g. disorientation).
With appropriate hardware/software components, the mobility devices not only compensate
for motoric impairments, but also provide orientation and navigation assistance. This variety is
a considerable challenge for assessment and configuration.
About 25 separate interlinked general domain ontologies have been developed (the layer below
Foundation, coloured red in Fig. 1) such as Medicine defining Disease, or Artifact defining
MobilityDevice as a subclass of Device.
Assistance: Intermediate Abstraction
Person
hasDisease
Disease
affects hasImpairment
Impairment hasAbility
requiresAssistance
impliesAbility needsMobilityDevice
compensatedBy providedBy
Ability Assistance Device
Figure 2: Composite• configuration for Person needsMobilityDevice
Ability compensatedBy Assistance MobilityDevice
• Assistance providedBy Device
Ontology Development Methodology – KSEM 2016 16
hasDisease_1Slight Person
hasDisease_2Moderate
hasDisease_3Severe
isAllowedDish_recommended
Disease isAllowedDish_1discouraged
isAllowedDish_2denied
hasDietRestriction_1Minor
isAllowedDish_3forbidden
hasDietRestriction_2Strict
requiresDiet isAllowedFOD_recommended
isAllowedFOD_1discouraged…
RestrictedDiet FOD Dish
allowsFOD_recommended comprisesConstituent _0VeryLow_or_greater_Intensity
allowsFOD_1discouraged comprisesConstituent _1Low_or_greater_Intensity
allowsFOD_2denied comprisesConstituent_2MediumHigh_or_greater_Intensity
allowsFOD_3forbidden comprisesConstituent_3High_or_greater_Intensity
comprisesConstituent_4VeryHigh_or_greater_Intensity
Figure 3: Configuration of Diet comprisesConstituent_5ExtremelyHigh_or_greater_Intensity
comprisesConstituent _6ExcessivelyHigh_or_greater_Intensity
Composite Configuration. To manage complexity, the relation needsMobilityDevice (Fig. 2)
is a stepwise composition of individual relations from Person to separately defined ontologies.
Individual Diseases of a particular Person are assessed, captured by the relation hasDisease. A
Disease may result in some Impairment modelled in the relation affects. In turn, an Impairment
impliesAbility an Ability; an Ability may be compensatedBy some Assistance providedBy some
MobilityDevice. For Disease, Impairment, etc., see [2].
Thus the relation needsMobilityDevice is sequentially composed, where each relation is a
“triangular” composition, e.g. requiresAssistance ∘ providedBy → needsMobilityDevice.
It is sufficient to model the individual relations affects, impliesAbility, etc., separately, and
once and for all; the rest is deduced by OWL-DL reasoners.
4. Configuration of Diet: Qualitatively Graded Relations
Similarly, the left-hand side of the diagram in Fig. 2 has been re-used and refined for the
configuration of diets, see Fig. 3.
To achieve a graded valuation according to some qualitative abstraction of a semantic concept,
we introduce extra valuation domains with values such as 1Slight, 2Moderate, 3Severe, or some
other (arbitrarily fine) qualitative metrics; the number of levels depends on the application.
Qualitatively graded relations (cf. [2, 3, 4, 5]) encode such valuations in the names of (a sheaf
of) relations hasDisease_1Slight, hasDisease_2Moderate, etc.
5. Structuring Food Products and Dishes
In the case of diets, the object to be configured is Food or Drink (abbreviated as FOD in Fig. 3); a
Person is recommended, ..., or forbidden certain FOD, e.g. food products containing PorkMeat,
or pungent dishes for Gastritis or certain allergies.
To facilitate such a modelling, the ontologies containing FOD and Dish are structured in an
onion-like fashion (Fig. 1): FOD_Source is the core, where e.g. PorkMeat is characterised as
isCutOf some Pig and fromAnimal SusScrofaDomesticus in the Biology ontology. Similarly,
FOD_Ingredient relates to FOD_Source only by the relation hasSource, and FOD_Product
to FOD_Ingredient by hasFODIngredient. A particular PungentDish can now be defined by
comprisesConstituent_2MediumHigh_or_greater_Intensity some PungentFlavourCompound,
etc. (Fig. 3). The BAALL Ontology is categorized on the basis of Eurocode-2 (EuroFIR), with
constituent relations to Aromas, the usual NutritionCompounds, and FoodAdditives (according
to the International Numbering System, INS).
6. Cooking Assistance
Early work on cooking assistance [3] has led to comprehensive modelling of Ingredients (cf.
Sect. 5). A recipe is a workflow of RecipeTasks such as preparation, baking, etc. Cooking is
modelled based on Ingredients with e.g. flavour properties, while RecipeTasks modify their
presence, e.g. when an aroma evaporates at a particular temperature. We hope to be able to
eventually predict a flavour composition in the final dish by “virtual cooking”.
7. Organizing Knowledge for Robotic Activities
The device configuration from Sect. 3 has later been refined by qualitatively graded configuration
(Sect. 4) and become the basis for configuration in robotics, sharing user and device profile
modelling, cf. [4].
The structure of an episodic memory has been formalised for domestic service robots per-
forming everyday activities (see [5]) by an ontology engineering effort undertaken by the
EASE1 project, resulting in a set of ontologies referred to as SOMA [6]. The focus of SOMA
is autonomous and cognitive robotics, a still emerging field, which differentiates it from the
more industrially focused effort at standardizing robotic knowledge representation undertaken
by the ORA group [7]. Researchers involved with the development of SOMA have built upon
knowledge and patterns formalized in the BAALL Ontology.
An important use of episodic memories is to provide training data for a robot to improve
its performance—either by learning from its own activities, or from observing (records of) the
activities of other agents, e.g. humans demonstrating how a task should be performed. An
episodic memory must be organized so that it will contain at least some useful knowledge for
an observing agent, even if the recording agent is different in body and capabilities. Therefore
it contains views on an activity from different levels of abstraction, from recordings of sensor
inputs and control signals, close to the hardware, to more cognitively motivated sequences of
tasks in which entities in the world play roles while obeying restrictions pertaining to the task,
or placed on the entities by their own dispositions and/or capabilities. At this cognitive level the
rich ontology of RecipeTasks and Cookware from the BAALL Ontology is put to use in EASE’s
episodic memories.
Gathering a rich variety of episodic memories requires an interdisciplinary collaboration
among roboticists, cognitive scientists, and neurologists in EASE1 . This places further critical
requirements on the knowledge infrastructure that processes episodic memories. In particular,
there must be ways to verify that episodic memories entered into storage obey logical constraints
on what is a well-formed episode recording; also important is to check whether an episode
recording represents a “good run” of an activity – episodes of failure are an interesting source
of learning, as long as failure is recognized as such [5].
8. Safe Extension Using Generic Ontology Design Patterns
Episodes and the other patterns above have been defined by Generic Ontology Design Patterns,
GODPs [2, 4, 5, 8], expressed in the language Generic DOL [9], an extension of the Distributed
Ontology, Model and Specification Language, an OMG standard (see omg.org/spec/DOL, dol-omg.
org), supported by the Heterogeneous Tool Set, Hets [10].
GODPs structure complex ontologies, with local confinement of design choices, ensuring
safety for modelling and data with sanity checks on their input and generative internal consis-
tency for their interrelation. Thus they are very useful for maintaining structural consistency in
extensions of large ontologies, even by non-experts.
We may e.g. define a pattern to extend FOD_Source with a new fish while relating it to
its counterpart in the Biology ontology; this anchor is important for correct translations to
common names in other natural languages, a likely source of error.
9. Conclusion
The BAALL Ontology is primarily used for semantic integration. Depending on the appli-
cation, several application domain ontologies provide restricted “views” to the structured
hyper-ontology (cf. Fig. 1). As examples consider Person importing Assistance and Artifact
for MobilityDevice on the one hand (cf. Sect. 3), or Person importing Medicine and Diet on
the other (cf. Sect. 4). Assistance for mobility devices, FOD, Diet, RecipeTask and Cookware
provide a fair coverage for extensive academic use; completeness for commercial use is not
claimed.
The structured BAALL Ontology and some examples of application domain ontologies derived
from it can be found at http://ontologies.baall.de, freely available to the academic community,
with a dual licence for commercial use.
It is presently being re-constructed by applying GODPs in a systematic fashion (cf. Sect. 8).
GODPs also support populating the BAALL Ontology by data in a safe way (cf. Sect. 8 and [5]),
e.g. for robot or human activity recordings (cf. Sect. 7), or for specific food products and dishes
(cf. Sect. 5).
Acknowledgments
We are grateful for their early contributions to Sidoine Ghomsi (aromas), Martin Rink [3],
Philipp Kolloge (diet), Dmytro Kozha (shopping assistance), and Marc Robin Nolte (bakery); to
Mihai Codescu and Till Mossakowski (Generic DOL); José deGea Fernandez (configuration in
robotics); and Daniel Beßler, Robert Porzel (episodes).
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