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.
• Disease • RestrictedDiet
• hasDisease • isAllowedDish
• DietRestriction • allowsFOD • allowsDish
• Universal Concepts • “Upper Ontology” DUL (DOLCE Ultra Light) without Axioms
• Eukaryota
• ChemicalCompound
• PhysicalQuantity
• FlavourCompound
• Price
• GeoRegion
• fromEukaryota • hasFlavourCompound
• hasSource
• hasFODIngredient
• ShoppingList
• PhysicalAspect • hasIngredient • comprises...
• PhysicalContainer • CookWare
• hasLowValue • hasHighValue
• RecipeTask • processFOD categories, characteristic relations, and axioms are only introduced by Generic Ontology Design Patterns when required, cf. Sect. 8.
The BAALL Ontology was motivated by the project Assistants for Safe Mobility (ASSAM, www.
assam-project.eu) [
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
affects
impliesAbility
hasDisease hasImpairment
hasAbility compensatedBy requiresAssistance
needsMobilityDevice providedBy
• Assistance providedBy Device
hasODnistoelaosgey_D1eSvleiglohptment Methodology – KSEM 2016
hasDisease_2Moderate Person
hasDisease_3Severe
Disease
hasDietRestriction_1Minor
hasDietRestriction_2Strict
requiresDiet isAllowedFOD_recommended
isAllowedFOD_1discouraged…
isAllowedDish_recommended
isAllowedDish_1discouraged
isAllowedDish_2denied
isAllowedDish_3forbidden
16
RestrictedDiet
allowsFOD_recommended
allowsFOD_1discouraged
allowsFOD_2denied
allowsFOD_3forbidden
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 afects . In turn, an Impairment
impliesAbility an Ability; an Ability may be compensatedBy some Assistance providedBy some
MobilityDevice. For Disease, Impairment, etc., see [
Thus the relation needsMobilityDevice is sequentially composed, where each relation is a “triangular” composition, e.g. requiresAssistance ∘ providedBy → needsMobilityDevice.
It is suficient to model the individual relations afects , impliesAbility, etc., separately, and once and for all; the rest is deduced by OWL-DL reasoners.
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. [
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).
Early work on cooking assistance [
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. [
The structure of an episodic memory has been formalised for domestic service robots
performing everyday activities (see [
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 diferent in body and capabilities. Therefore it contains views on an activity from diferent 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 [
Episodes and the other patterns above have been defined by Generic Ontology Design Patterns,
GODPs [
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 consistency 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.
The BAALL Ontology is primarily used for semantic integration. Depending on the application, 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 [
We are grateful for their early contributions to Sidoine Ghomsi (aromas), Martin Rink [