Re: KR 101 - Spatial Ontology Baseline – conceptual alignment and pointers

Paola, all,

Thank you for the pointer to Bateman & Farrar’s Spatial Ontology 
Baseline (SFB/TR8 D2). I’ve gone through it and here is a compact 
conceptual synthesis plus some pointers to open vocabularies/standards, 
and how this connects to what I’m doing with spatial KR in K3D.

1. What D2 is doing conceptually

D2 sits in a sequence (D1–D4) and focuses specifically on space as an 
ontological category. In my reading, the key points are:

Space as an ontological category
Space is treated explicitly, not just as coordinates: we need to 
characterize spatial entities (regions, objects, features), their parts, 
boundaries, and the relations between them.

Foundational tools: mereology, topology, geometry, mereotopology
The report reviews how parthood, connection, boundary, overlap, 
interior, etc. can be axiomatized and combined:

mereology → part/whole;
topology → continuity, connectedness;
mereotopology → parthood + connection (e.g., Cohn/Lehmann);
geometry → metric and shape.
Region‑based vs object‑based representations
A central theme is region‑based calculi (e.g., RCC) vs object‑based 
accounts, and how region connection, contact, overlap, containment and 
vagueness (egg‑and‑yolk examples) should be represented.

Survey across upper/domain ontologies
It walks through spatial aspects of major ontologies:

SUMO‑space: SpatialThing, Region, SelfConnectedObject, ShapeAttribute, 
SpatialRelation taxonomies.
OpenCyc‑space: spatial parts, spatial relations, path/path systems.
DOLCE‑space: physical objects vs features, spatial dependence, qualities.
BFO‑space: SNAP/SPAN, SpatialRegion, Site, niches, layers, granularity.
It then connects these to qualitative spatial representation, GIS, 
cognitive semantics and spatial language.
Conclusions and recommendations
The final sections stress:

the importance of observer viewpoint;
bridging formal spatial ontologies with spatial language and GIS;
modular foundations (upper ontologies) and reusable submodules;
and the need for a generic framework with clear categories and mapping 
between layers.
So, to paraphrase: D2 is about how to carve up space in ontology using 
region‑based and object‑based tools, how existing upper ontologies 
(SUMO, DOLCE, BFO, etc.) do it, and how that should inform practical 
modeling.

2. Pointers to open vocabularies and standards (spatial KR)

D2 itself points to several “upper” and spatial ontologies that are 
still relevant:

SUMO (Suggested Upper Merged Ontology) – open upper ontology with 
spatial categories (SpatialThing, Region, ShapeAttribute, SpatialRelation).
DOLCE / DOLCE+ – foundational ontology from LOA, with clear treatment of 
physical objects, features, and spatial dependence.
BFO (Basic Formal Ontology) – widely used upper ontology (SNAP/SPAN, 
SpatialRegion, Site, niches).
Region Connection Calculus (RCC) – qualitative spatial calculus for 
regions and their relations (RCC‑5, RCC‑8).
GIS & OGC/ISO standards – e.g. ISO 19107 for geometry/topology; 
GeoSPARQL for spatial relations in RDF; OWL Time for temporal aspects.
On the web/AI side, I would add:

GeoSPARQL: OGC standard for geographical features and topological 
relations in RDF.
OWL Time: temporal ontology often combined with spatial KR.
PROV‑O: for provenance of spatial/temporal artifacts.
These provide the “upper” and mid‑level vocabularies into which more 
specialized domain ontologies can be grounded.

3. How this connects to what I’m doing (and where K3D sits)

K3D is not trying to replace any of that; it is trying to instantiate it 
in a spatial, inspectable substrate:

Houses and Rooms as domains of discourse
Each House is a bounded domain (in the sense Milton, and D2’s “domain 
ontologies,” use the term); Rooms partition that domain further. 
Conceptually, these can be grounded in upper ontologies like DOLCE/BFO 
(e.g., PhysicalRegion, Site, SpatialRegion) and specialized out with 
SUMO/DOLCE/BFO categories as needed.

Nodes and Galaxies as spatial representations of concepts and relations
Nodes are the things we place in space; Galaxies are the embedding 
spaces where distances encode similarity, but the categories (object vs 
region, path, feature, etc.) can be taken from or aligned with the 
ontologies D2 surveys.

Garden and Museum as structuring devices
The Knowledge Garden is a fractal, tree‑like visualization of ontologies 
(roots/branches/leaves) that can be populated with DOLCE/BFO/SUMO 
categories; the Museum uses “portal cubes” to hold large, archived 
ontologies or GIS‑like datasets consistent with the layered approach D2 
advocates.

I’ve documented the visual/structural encoding here:

docs/SPATIAL_KR_VISUAL_ENCODING.md – how domains, concepts, relations, 
modalities and time are encoded as shapes, rays, trees and cubes in 
K3D’s Galaxy, Garden and Museum, independent of any particular GPU backend.

In other words, I see D2 as a conceptual baseline for how we talk about 
space in ontologies (upper and domain), and K3D as one concrete way to 
host and visualize those ontologies (and their relations, and their 
temporal/adequacy status) in a shared 3D substrate for humans and AI.

I agree entirely that domain ontologies must be grounded in upper 
ontologies; I’m not arguing against that. My contribution is to provide 
a spatial KR framework where that grounding and those relations become 
visible and navigable, while still being compatible with the ontological 
work D2 summarizes.

Best regards,
Daniel