# Enabling Biodiversity-Informed Architecture Through Ontology-Driven Data Integration
###### Abstract
In our case, we have modelled ECOOPES ontology using a top-down conceptualisation, a bottom-up approach informed by the data, and by reusing structures from well-established ontologies, namely OBO Relations [16] or Darwin Core [[https://dwc.dtwg.org](https://dwc.dtwg.org)]([https://dwc.dtwg.org](https://dwc.dtwg.org)) (accessed on 17 March 2025) following ontology construction best practices. The proposed ontology, developed in collaboration with domain experts and adhering to Semantic Web and Linked Data best practices, serves as a mediator between life sciences data (e.g., species distribution and habitats) and geometric information (e.g., maps, voxel models of building structures). The ontology is an interface with CAD and aids the design of ecological building envelopes addressing the critical \"data-to-design gap\" [17; 18] in computational architecture and multi-species architectural design. Constraints, unlike ontologies--which are designed for logical, deductive reasoning-adhere to the Closed World Assumption (CWA), which precisely defines how data should be structured by imposing specific restrictions on the data model. In our case, constraints are represented as explicit facts in the knowledge graph, such as the solar requirements of plants or the minimum and maximum proximity distances between species. By using standards like SHACL [[https://www.w3.org/TR/shacl/](https://www.w3.org/TR/shacl/)]([https://www.w3.org/TR/shacl/](https://www.w3.org/TR/shacl/)) (accessed on 17 March 2025), one can define and validate the expected \"shapes\" and structure of the data, ensuring data quality and completeness while also enabling the enforcement of consistency across the dataset. In our case, validation is currently performed using a set of SPARQL queries. The constraints in our problem setting can be translated to SPARQL queries, e.g., _\"Provide me the locations in site where I can place the plant Abies alba (EcoNode) such that its solar requirements are met?\"_. Given the spatial nature of this query, typically, one would employ a GeoSPARQL query to answer it; nonetheless, in our case, the voxel model containing x, y, and z coordinates boils down to _equality checking_ with respect to CAD coordinates. The computational model that is used to generate design outcomes based on the designer's input is rule-based. This approach computes the design outcomes based on the selected rules that can be run in a designated order. Our current implementation mimics rule-based reasoning by employing a set of SPARQL update queries [19] in the context of OBDM that are run in a sequence that is orchestrated by the Grasshopper environment. This integration enables the adaptation of sites, buildings, and geometries, respectively, to create habitats that attract and support urban wildlife, contributing to ecological sustainability. The paper illustrates the practical utility of the ontology through a (case study) demonstrator, highlighting its role in guiding building designs that promote species attractiveness and urban biodiversity. Our contributions are the following:  
* A new EIM (ECOLOPES Information Model) ontology and knowledge graph in the domains of architecture and ecology. * A method that utilises OBDM to map and integrate different data sources--stemming from different environments from the respective domains--through an ontology, effectively creating the knowledge graph. OBDM enables query and update operations to be performed directly on the integrated data. * A designer workflow, in which the designer obtains feedback (e.g., the fulfilment of solar radiation or proximity constraints) or asks CQs against the knowledge graph, enabled by OBDM. The rest of the paper is structured as follows. In Section 2, we provide the necessary background, motivation, and problem statement. In Section 3, we discuss related work with respect to using ontologies in design and ecology. In Section 4, we explain the ontology design, reuse, and alignment.