Article: KNOWLEDGE GRAPH CONSTRUCTION FOR SUBSURFACE OBJECTS INCLUDING UNCERTAINTY AND TIME VARIATION
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: Nanopublication
created: 2026-08-26T08:53:07.000Z
creator: Ashley Caselli
license: https://creativecommons.org/licenses/by/4.0/
has nanopub type: ScholarlyWork
introduces: KNOWLEDGE GRAPH CONSTRUCTION FOR SUBSURFACE OBJECTS INCLUDING UNCERTAINTY AND TIME VARIATION

KNOWLEDGE GRAPH CONSTRUCTION FOR SUBSURFACE OBJECTS INCLUDING UNCERTAINTY AND TIME VARIATION
abstract: In the recent years the concept of knowledge graph has emerged as a way to aggregate information from various sources without imposing too strict data modelling constraints. Several graph models have been proposed during the years, ranging from the “standard” RDF to more expressive ones, such as Neo4J and RDF-star. The adoption of knowledge graph has become established in several domains. It is for instance the case of the 3D geoinformation domain, where the adoption of semantic web technologies has led to several works in data integration and publishing. However, yet there is not a well-defined model or technique to represent 3D geoinformation including uncertainty and time variation in knowledge graphs. In this paper we propose a model to represent parameterized geometries of subsurface objects. The vocabulary of the model has been defined as an OWL ontology and it extends existing ontologies by adding classes and properties to represent the uncertainty and the spatio-temporal behaviour of a geometry, as well as additional attributes, such as the data provenance. The model has been validated on significant use cases showing different types of uncertainties on 3D subsurface objects. A possible implementation is also presented, using RDF-star for the data representation.
date: 2021-10-07
is part of: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author list: author-list
page end: 136
page start: 131
volume: XLVI-4/W4-2021
type: ResearchPaper

Ashley Caselli
affiliation: University of Geneva

Gilles Falquet
affiliation: University of Geneva

Claudine Métral
affiliation: University of Geneva

sig
has algorithm: RSA
signed by: Ashley Caselli