# Enabling Biodiversity-Informed Architecture Through Ontology-Driven Data Integration
###### Abstract  
Urban biodiversity is essential for sustainable cities, as it helps address the challenges of environmental degradation, ecosystem loss, species decline, and increased vulnerability to climate hazards, which negatively affect human health and well-being. ECOOPES (ECological building envelopPES) aims to develop a design approach for multi-species as stakeholders to achieve regenerative urban ecosystems. Integrating the diverse data required for stakeholders and beyond--spanning the life sciences, geography, and architecture--and utilising it for design presents a significant challenge. This paper introduces an ontology-driven approach that utilises ontology-based data management (OBDM) as a framework for integrating diverse data sources, enabling ecologists and architects to design sites and buildings that foster urban biodiversity. OBDM offers a unified view of multiple data sources through an ontology, enabling query and update operations to be performed directly on the integrated data. 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 and voxel models of building structures). 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, highlighting its role in guiding building designs that promote species attractiveness and urban biodiversity. on t o t o o t o o t o o o t o  
architectural factors. These requirements and constraints must be clearly defined, formalised, and placed in the appropriate context to ensure effective integration within urban and architectural planning. Ontologies [2] provide a structured approach to representing knowledge by defining concepts and relationships within and across various domains. Additionally, they enable axiomatic reasoning, allowing implicit facts to be inferred from explicitly stated information within a knowledge base [3]. Implementing a knowledge base using knowledge graphs [4] offers greater flexibility in data integration, as they are built upon well-established Semantic Web and Linked Data principles--such as IRIs for uniquely identifying entities and RDF for representing facts in the form of triples, ensuring interoperability and semantic richness in data integration processes. Integrating the diverse data required for stakeholders and beyond--spanning life sciences, geography, and architecture--and utilising it for design presents a significant challenge. This paper introduces an ontology-driven approach that utilises ontology-based data management (OBDM) [5] as a framework that integrates diverse data sources, enabling ecologists and architects to design sites and buildings that foster urban biodiversity. The proposed ontology-aided design methodology builds upon the concept of performance-oriented design [6; 7] and the conceptual framework focused on data-driven approaches to understanding and designing environments [8]. Data sources range from publicly available datasets, which can be easily accessed and retrieved via web APIs--such as GLoBI (Global Biotic Interactions for species) [9]--to private datasets generated within ECODPES, including Plant Functional Groups (PFGs) [10], Animal Functional Groups (AFGs), and voxel data [11] that capture site-specific variables like coordinates, aspect, slope, or solar radiation. Plant and animal functional groups, defined by shared traits or ecological roles rather than taxonomic classification, play a crucial role in ecosystem processes, resilience, and biodiversity conservation. A voxel model contains useful aggregated information applicable to various use cases, such as computing the _Habitat Suitability Index_ (HSI), a model which evaluates how well a habitat suits a given species.