Darren's Knowledge Garden 🪴

Bioregional Knowledge Commoning - Part 1: Foundations and Participatory Ontology Design

May 22, 202545 min read

This is Part 1 of a 3-part series on Bioregional Knowledge Commoning. Part 2 covers technical architecture for implementing BKC systems. Part 3 explores governance and sustainability models.

Introduction

The convergence of ecological crisis, technological possibility, and renewed interest in place-based governance presents a unique opportunity to reimagine how communities create, share, and steward knowledge about their bioregions. This series explores the development of a Bioregional Knowledge Commons (BKC) - a community-stewarded, decentralized knowledge ecosystem designed to foster deeper understanding, sustainability, and resilience within specific “life-places.”

In this first part, we establish the conceptual foundations and explore how communities can collaboratively design the semantic structures that will organize their collective knowledge.

Section 1: Conceptual Foundations of the Bioregional Knowledge Commons (BKC)

The development of a Bioregional Knowledge Commons (BKC) necessitates a robust conceptual framework that clearly defines its constituent parts: the bioregion and the knowledge commons. This section establishes this theoretical underpinning, exploring the ecological, social, and cultural dimensions of bioregions, the principles guiding knowledge commons, and culminating in a synthesized vision for the BKC. This foundation is crucial for understanding the ‘why’ and ‘what’ of a BKC (situating it within broader explorations of protocols for regenerative civilization) before subsequent sections delve into the ‘how’ of its design, implementation, and stewardship.

1.1. Understanding Bioregions: Ecological, Social, and Cultural Interconnections

The concept of a bioregion serves as the geographical and philosophical anchor for the BKC. A bioregion is more than just a geographical area; it is a “life-place”1, a unique territory defined by an intricate interplay of natural characteristics and the human cultures and systems that have co-evolved within these specific environmental contexts.1 It represents an integrated understanding of ecological and human systems, inviting a relational way of inhabiting a place.2

From an ecological systems perspective, bioregions are frequently delineated by natural boundaries, most notably watersheds and hydrological systems. These definitions incorporate a layered understanding of the Earth’s features, ranging from fundamental geology, topography, and tectonics to the dynamic elements of soil composition, diverse ecosystems, and prevailing climate patterns.1 The One Earth initiative, for instance, provides a scientific framework that positions bioregions as significant subdivisions of larger biomes. These bioregions are identified by intersecting these biomes with large-scale geological formations such as mountain ranges and basins, as well as commonly accepted climate zones.2 This ecological definition provides a tangible, earth-systems basis for understanding the unique environmental context of a bioregion. For example, the South Devon bioregion in the UK is mapped based on river catchments, extending to natural dividers like the River Tamar and the uplands of Dartmoor where its main rivers originate.2 Similarly, Cascadia in North America is defined by the watersheds of major rivers like the Columbia and Fraser.2

However, the definition of a bioregion extends significantly beyond purely ecological parameters to deeply incorporate human systems and cultural dimensions. Bioregionalism as a philosophy underscores that the identification and understanding of a bioregion are inherently cultural phenomena. This perspective places strong emphasis on local populations, their accumulated knowledge, unique ways of life, and context-specific solutions.1 A bioregion, therefore, encompasses not only flora and fauna but also human settlements, distinct cultures, and traditional ways of knowing and interacting with the land. This includes shared community understandings of territorial boundaries, local ecological management practices, and the stories, songs, and landmarks that imbue a place with meaning.1 Author Kirkpatrick Sale’s influential definition explicitly includes attributes of flora, fauna, water, climate, soils, and landforms, alongside the crucial elements of “human settlements and cultures those attributes have given rise to”.1 This holistic view recognizes that human culture is not separate from, but rather an integral part of, the bioregional fabric.

A central tenet in understanding bioregions is the concept of interconnectedness, sometimes referred to as “interbeing”.2 This principle highlights the complex and reciprocal relationships between all living and non-living elements within the bioregion. It calls for a shift in perspective towards inhabiting a place in a way that is deeply relational (a theme explored in rethinking data for a relational age) and aware of these myriad connections.2 Bioregionalism, in this sense, seeks to foster a harmonious relationship between human culture and the natural environment, viewing humanity not as a dominant or separate force, but as an intrinsic component of nature itself.1 This perspective moves beyond seeing nature merely as a collection of resources to be managed, towards recognizing it as a web of life to which humans belong and have a responsibility.

The scope and purpose of bioregionalism extend from philosophical ideals to practical applications. It is a philosophy proposing that political, cultural, and economic systems achieve greater sustainability and justice when they are organized around these naturally defined bioregions.1 A key aim is to align human-imposed political boundaries with inherent ecological boundaries, thereby fostering governance structures that are more responsive to environmental realities. Bioregionalism also seeks to highlight and celebrate the unique ecological characteristics of each region, encouraging local sustainability practices such as the consumption of local foods and materials, and the cultivation of native plant species.1 Fundamentally, it endeavors to connect humanity more deeply to the specificities of place.3 This connection is not passive; bioregionalism is increasingly understood as an active, participatory process where the land itself—its features, capacities, and limits—becomes the foundational starting point for design, planning, and community action.4 The P2P Foundation, for example, views bioregionalism as a comprehensive framework for living sustainably and respectfully within a particular place, advocating for a remapping of governance structures based on natural boundaries, which in turn empowers local communities.5 Practitioners in the field are moving beyond bioregionalism as a static “ism” towards “bioregioning” – an active and ongoing engagement with concepts of reinhabitation, ethical interaction with more-than-human landscapes, and deliberate, place-based change.6

1.2. The Knowledge Commons Paradigm: Principles for Shared Bioregional Understanding

Parallel to the concept of the bioregion, the “knowledge commons” paradigm offers a framework for managing and sharing information and understanding. A knowledge commons refers to the institutionalized community governance of sharing, and in some instances, the co-creation of a wide array of intellectual and cultural resources (see further sources and commentary on this concept). These resources include information, scientific findings, diverse forms of knowledge, data, and other intangible assets.7 UNESCO articulates a core principle of knowledge commons, stating that they specify that knowledge is made available to all individuals for their collective benefit, enabling them to address both material and intangible challenges as they shape their desired futures.8

Several core principles underpin the knowledge commons model. Firstly, shared governance is paramount, emphasizing community-led management and decision-making regarding the knowledge resources.7 This contrasts with top-down or proprietary control over information. Secondly, accessibility is fundamental; knowledge within a commons is intended to be available to all members of the relevant community for their use and benefit.8 Thirdly, the development and operation of knowledge commons should be guided by evidence-based policymaking. This involves basing policies and operational rules on a thorough understanding of how commons institutions effectively function, often drawing upon foundational research such as Elinor Ostrom’s Nobel Prize-winning work on the governance of natural resource commons.7 This research highlights principles like clearly defined boundaries, congruence between rules and local conditions, collective-choice arrangements, monitoring, graduated sanctions, conflict-resolution mechanisms, and recognized rights to organize.

The P2P Foundation’s perspective on knowledge commons provides a contemporary example of these principles in action. Their conceptualization of a Knowledge Commons describes it as a vast and dynamic digital sector primarily focused on ensuring that resources are shared and freely available. It emphasizes the co-creation of knowledge and the provision of spaces, both digital and potentially physical, that facilitate discussion and collaboration.9 The P2P Foundation’s model operates on core tenets of open access, collaborative production of knowledge, and community-driven evolution of the commons itself.9 Key characteristics of this approach include the creation of common goods through open and participatory processes of production and governance. Universal access to these goods is typically guaranteed through the use of open licensing mechanisms, such as Creative Commons licenses.10 This framework ensures that knowledge is not only shared but also actively enriched and maintained by its community of users and contributors.

1.3. Defining the Bioregional Knowledge Commons (BKC): Vision, Scope, and Potential

Synthesizing the concepts of bioregions and knowledge commons leads to the definition of a Bioregional Knowledge Commons (BKC). A BKC is, at its core, a knowledge commons specifically dedicated to the diverse forms of knowledge pertaining to a particular bioregion. It aims to integrate the rich tapestry of ecological, social, and cultural knowledge that is unique to that specific “life-place”.1 This integrated knowledge is then transformed into a shared, collectively governed resource for the benefit of the community inhabiting and stewarding that bioregion.

The vision for a BKC is to empower bioregional communities by providing them with shared, accessible, and co-created knowledge. This empowerment is intended to foster deeper understanding of their environment and heritage, promote sustainable practices, enhance resilience to various challenges, and cultivate a stronger connection to their place. A BKC directly supports the concept of “reinhabitation,” which involves individuals and communities becoming truly native to a place through a heightened awareness and understanding of the particular ecological relationships and cultural narratives that operate within and around it.6

The scope of a BKC is necessarily broad and inclusive, reflecting the multifaceted nature of a bioregion. It would encompass a wide array of knowledge types, including:

  • Ecological Data: Detailed information on flora, fauna, water systems, soil types, climate patterns, and geological features specific to the bioregion.1

  • Local and Indigenous Knowledge: Place-based wisdom, traditional ecological knowledge (TEK), sustainable land management practices, and cultural narratives passed down through generations.1

  • Cultural Heritage: Documentation of stories, songs, traditional pathways, historical landmarks, and other culturally significant sites and practices that define the bioregion’s human dimension.1

  • Scientific Research: Findings from academic studies, environmental assessments, and other scientific investigations relevant to the bioregion, potentially managed using DeSci graph methodologies.

  • Community-Generated Data: Information collected by citizen scientists, local groups, and individuals through participatory monitoring or mapping projects.

  • Historical Information: Archival records, oral histories, and other materials that illuminate the bioregion’s past.

The potential and purpose of a BKC are manifold, offering significant benefits to its community:

  • It can facilitate collaborative learning and problem-solving by providing a shared platform where diverse stakeholders can access information, share insights, and work together to address complex bioregional challenges, such as climate change adaptation or resource management.

  • It can support sustainable development and resource management that are aligned with the unique characteristics and carrying capacities of the bioregion.1

  • It can strengthen local economies and cultural practices by making relevant knowledge more accessible (a principle aligned with cosmolocalism), supporting local innovation, and preserving cultural heritage.5

  • It has the potential to enhance community resilience to environmental, social, and economic changes by equipping communities with the knowledge needed to anticipate, adapt, and respond effectively.11

  • A BKC can serve as a dynamic platform for “bioregioning”—the active, participatory process of living regeneratively and sustainably within a bioregion.4

  • The functions of a BKC align closely with initiatives like UNESCO Biosphere Reserves, which are conceived as ‘learning places for sustainable development’.12 A BKC could provide the critical knowledge infrastructure to support and amplify the work of such reserves and similar bioregional initiatives.

The inherent characteristics of bioregionalism, with its focus on interconnected, place-based ecological and social systems, create a naturally conducive environment for applying knowledge commons principles. The very idea of a bioregion as a “life-place”1 implies a need for shared understanding, collective stewardship, and a deep, relational way of inhabiting that place2 – all of which are central tenets of a knowledge commons.7 Thus, a Bioregional Knowledge Commons is not merely a convenient juxtaposition of two concepts; it represents a logical and potent extension where the specific knowledge of the bioregion becomes the “commons” to be curated, enriched, and utilized by its inhabitants. This implies that the design, governance, and evolution of a BKC must be deeply reflective of both the ecological specificities and the unique cultural and social dynamics of the bioregion it serves.

Furthermore, bioregionalism is increasingly understood not just as a way of describing the world, but as a call to action—an “active, participatory process”4 aimed at “regenerative living”.6 This active stance involves practical efforts like reimagining governance structures, restoring ecosystems, and empowering local communities.5 Such endeavors demand an accessible, comprehensive, and relevant knowledge base pertaining to the bioregion’s ecology, existing practices, inherent challenges, and emerging opportunities. A BKC, by its very nature as a shared knowledge repository and a collaborative platform7, is ideally positioned to provide this critical infrastructure. Consequently, the BKC transcends the role of a passive archive; it becomes an active enabler and catalyst for the regenerative and participatory aspirations of contemporary bioregionalism, supporting a wide spectrum of activities from promoting local food consumption1 to coordinating large-scale ecosystem restoration efforts.5

Section 2: Designing the BKC Ontology: A Participatory and Inclusive Approach

The heart of a functional and meaningful Bioregional Knowledge Commons lies in its ontology. An ontology provides the semantic backbone, defining the concepts, entities, and relationships that structure the diverse knowledge within the BKC. This section details the critical role of ontology, emphasizing collaborative and inclusive development processes. It particularly focuses on “ontology commoning,” the respectful and ethical integration of Indigenous Knowledge Systems (IKS), the application of advanced AI tools for ontology enrichment, and the necessity of embracing ontological pluralism.

2.1. The Crucial Role of Ontology in Structuring Bioregional Knowledge

In the field of information science, an ontology is understood as a formal, explicit specification of a shared conceptualization. It involves the representation, formal naming, and precise definition of categories, properties, and the relationships that exist between various concepts, data elements, or entities pertinent to a specific domain (for instance, using a standardized URI scheme for bioregions to identify them).13 Essentially, an ontology serves as a structured vocabulary and a set of relational expressions that map out the entities within a subject area and how they interrelate.13 For the Bioregional Knowledge Commons, the ontology will establish a shared semantic framework (aiming for high semantic density), providing a common language and structure for the vast and diverse array of bioregional data it aims to encompass.

The benefits of a well-designed ontology for the BKC are substantial:

  • Semantic Interoperability: A core advantage is the enablement of semantic interoperability. This means that diverse knowledge systems and data sources—spanning ecological data, cultural narratives, scientific research, and community observations—can be understood, integrated, and harmonized coherently within the BKC.14 Without such a framework, these disparate knowledge sets would remain siloed and difficult to synthesize.

  • Knowledge Discovery and Reasoning: The structured nature of an ontology facilitates advanced querying, sophisticated analysis, and logical inference over the comprehensive bioregional knowledge base.15 This allows users and AI systems to uncover patterns, derive new insights, and answer complex questions that would be intractable with unstructured data.

  • Shared Understanding: An ontology creates a common conceptual framework that enables community members, researchers, policymakers, and AI systems to interpret, contribute, and utilize knowledge in a consistent manner.16 This shared understanding is vital for effective collaboration and collective action.

  • Data Integration: It provides the necessary structure to support the integration of heterogeneous data flowing from various sources. This includes the challenge of incorporating multimedia content (images, audio, video) by transforming it into a structured format that can be linked within the ontological framework.17

Several types of ontologies are particularly relevant to the development of a BKC ontology:

  • Domain Ontologies: These are specific to particular aspects or sub-domains within the bioregion. Examples include ontologies for local ecology (e.g., detailing species, habitats, and ecological processes)17, sustainable development (e.g., modeling goals, indicators, and interventions)18, urban and regional planning (e.g., representing land use, infrastructure, and planning policies)19, and, crucially, Indigenous Knowledge Systems (e.g., capturing concepts, relationships, and protocols specific to Indigenous worldviews).20

  • Upper Ontologies: These provide a high-level, foundational structure of general concepts and relations that can be used to ensure consistency and interoperability across different domain-specific modules within the broader BKC ontology.13 Well-established upper ontologies like the Basic Formal Ontology (BFO) or the Common Core Ontologies (CCO)18 can serve this purpose. For instance, the Sustainable Development and Climate (SDC) ontology, which models UN SDG targets, is built upon the CCO and, by extension, BFO, demonstrating a practical application of this hierarchical approach. 18

By carefully designing and implementing these ontological layers, the BKC can achieve a robust, flexible, and semantically rich knowledge infrastructure capable of supporting its diverse goals.

2.2. ‘Ontology Commoning’: Co-creating Meaning through Community Workshop Insights

The development of the BKC ontology cannot be a purely top-down or expert-driven endeavor. To truly reflect the bioregion and serve its community, a participatory approach termed ‘ontology commoning’ is essential. This concept refers to a collaborative, community-driven, and iterative process of developing and evolving ontologies, placing strong emphasis on shared ownership of the semantic structures and participatory meaning-making.21 It prioritizes the “ongoing organic growth of common ontology” rather than the imposition of a fixed, immutable definition from a central authority.21 This philosophy aligns closely with the P2P Foundation’s principles of open, participatory production and governance in the creation of common goods.10

The importance of ontology commoning for the BKC is multifaceted. Firstly, it ensures that the resulting ontology genuinely reflects the diverse perspectives, values, and forms of knowledge held by the bioregional community. This includes capturing local terminologies, Indigenous understandings of relationships, and community-specific conceptualizations that might be overlooked by external experts. Secondly, such a participatory approach is crucial for building trust, fostering a sense of ownership among community members, and ensuring the legitimacy and relevance of the BKC as a whole. When the community co-creates the semantic framework, they are more likely to adopt, use, and contribute to the commons built upon it.

Several methodologies for participatory ontology engineering can inform the BKC’s approach:

  • Human-Centered Ontology Engineering (HCOME): This class of methodologies explicitly accentuates the active and continuous participation of domain experts—in this case, bioregional community members and knowledge holders—throughout the entire ontology lifecycle, from initial specification to ongoing maintenance.15

  • The ACCIO Project Methodology: This provides a practical example of a middle-ground approach that effectively involves social scientists (to facilitate community engagement and understand social contexts), ontology engineers (to provide technical expertise), and diverse stakeholders (such as community members, local practitioners, and Indigenous representatives) in every stage of ontology development: Specification, Conceptualization, Formalization, Implementation, and Maintenance.15

  • Key stakeholder engagement techniques from the ACCIO methodology include15:

  • Forming representative stakeholder groups that reflect the community’s diversity.

  • Conducting hands-on workshops to build a shared understanding of ontology concepts and collaboratively define initial requirements.

  • Performing ethnographic observations of daily practices and conducting interviews to capture existing knowledge and workflows.

  • Utilizing mind maps to systematically organize and represent observations for community validation.

  • Developing “sunny-day scenarios” (ideal use cases) and personas (representative user profiles) to clearly define the scope and user requirements for the ontology.

  • Organizing role-play co-design workshops where stakeholders enact scenarios to collaboratively build the conceptual model of the ontology.

  • Holding decision-tree co-design workshops to formalize complex decision-making processes and extract rules for the ontology.

  • Community-Based Ontology Development Tools: The emergence of platforms designed for collaborative ontology work is also significant. For example, Ontokiwi, an extension of the MediaWiki software (the engine behind Wikipedia), aims to support community-wide ontology annotation and development by leveraging familiar wiki-style collaboration features.22 Similarly, WebProtégé is a web-based tool that facilitates collaborative ontology editing by multiple users simultaneously.23

Incorporating community workshop insights is a cornerstone of ontology commoning. Workshops, designed using techniques like those from the ACCIO project, will be crucial throughout the BKC ontology development lifecycle. These workshops will serve to:

  • Collectively identify the key entities, concepts, attributes, and relationships that are most relevant and meaningful to the bioregion and its inhabitants.

  • Collaboratively define terms and their nuanced meanings from local and Indigenous perspectives, ensuring the ontology’s vocabulary is grounded in community understanding.

  • Provide a forum for validating, reviewing, and iteratively refining ontology components as they are developed.

  • Facilitate dialogue to resolve differing viewpoints, negotiate meanings, and achieve a degree of consensus on how knowledge should be represented, thereby fostering a truly shared semantic framework.

This commitment to ontology commoning ensures that the BKC’s semantic structure is not an abstract technical artifact but a living reflection of the bioregion’s collective intelligence and diverse ways of knowing. Such a process is fundamental for the BKC’s success, as it directly addresses the need for legitimacy, trust, and the genuine co-creation of shared understanding, particularly when integrating sensitive and diverse knowledge forms like Indigenous Knowledge. It shifts the power dynamic from expert-led design to a more democratic and culturally attuned co-construction of meaning within the commons.

2.3. Integrating Indigenous Knowledge Systems (IKS): Protocols, Ethics, and Ontological Respect

A critical and defining aspect of the Bioregional Knowledge Commons ontology is the respectful and ethical integration of Indigenous Knowledge Systems (IKS). IKS represents a living body of knowledge, practices, and beliefs, developed and accumulated by Indigenous Peoples over millennia through direct experience and intimate relationship with their local environments and cultural contexts.24 This knowledge is often transmitted orally from generation to generation, is typically collectively owned within the community, and encompasses profound ecological wisdom, sustainable resource management practices, unique cultural values, and distinct worldviews.24

The importance of integrating IKS into the BKC cannot be overstated. IKS holds invaluable insights essential for a holistic understanding of the bioregion, contributing significantly to efforts towards sustainability, climate resilience, and culturally relevant resource management. A BKC that fails to meaningfully include IKS would be incomplete and perpetuate historical patterns of exclusion.

The integration process must be guided by stringent ethical protocols and best practices, developed in full partnership with Indigenous communities25:

  • Free, Prior, and Informed Consent (FPIC): This is an absolute prerequisite. No engagement with Indigenous communities or gathering of their knowledge should occur without their explicit, uncoerced consent, given after they have received full and culturally appropriate information about the BKC project, its intentions, potential uses of their knowledge, and their rights.25

  • Respect, Humility, and Relationship Building: Non-Indigenous partners must approach engagement with genuine respect, humility, and a willingness to learn from and adapt to Indigenous ways of knowing. IKS must be recognized as equally valid to Western scientific knowledge systems.25 Building trusting, long-term relationships is paramount.

  • Community Ownership and Control (OCAP® Principles): The principles of Ownership, Control, Access, and Possession (OCAP®) regarding Indigenous data and knowledge must be upheld. This means that Indigenous communities themselves own their collective knowledge, have control over how it is used and managed, must be able to access their data within the BKC, and should have possession (or determine stewardship) of their data.25

  • Indigenous Data Sovereignty (IDSov): This is the inherent right of Indigenous Peoples to govern the collection, ownership, application, interpretation, and stewardship of data pertaining to their peoples, lands, resources, and knowledge systems.26 IDSov must be a foundational, non-negotiable principle guiding the BKC’s architecture, ontology, and governance concerning IKS.

  • Culturally Appropriate Methods: The methods used for sharing, documenting, and representing IKS must be co-designed with and deemed appropriate by the Indigenous communities involved. This may include storytelling, land-based sharing, elder consultations, community-led verification processes, and the use of Indigenous languages.25

  • Compensation and Reciprocity: Elders, Knowledge Keepers, and other community members must be fairly and respectfully compensated for their time, expertise, and the sharing of their knowledge, recognizing it as a valuable contribution.25 Reciprocity also involves ensuring that the BKC provides tangible benefits back to the Indigenous communities.

  • Contextual Integrity: Indigenous Knowledge must be kept within its proper cultural and spiritual context. Efforts should be made to avoid decontextualization, misrepresentation, or forcing IKS into incompatible Western frameworks or ontological categories.25

  • Benefit Sharing: Mechanisms must be established to ensure that any benefits arising from the use of IKS within the BKC (e.g., new insights, resource management strategies) are shared equitably with the originating Indigenous communities.

Beyond procedural ethics, there are profound ontological considerations for representing IKS within the BKC:

  • Ontological Pluralism: It is crucial to acknowledge that IKS often arises from ontological presuppositions—fundamental assumptions about the nature of reality, being, and existence—that may differ significantly from those underpinning Western scientific ontologies.20 For example, the Stó:lō Nation’s understanding of Stone T’xwelátse not as an inanimate object but as a living ancestral being directly challenges conventional Western object-based ontologies.27 Similarly, concepts like Amazonian multinaturalism, which posits a single, shared “human” culture extending to animals and spirits, with different worlds instantiated by varying bodily perspectives, represent a fundamentally different way of categorizing beings and their relationships compared to a singular, objective material world often assumed in Western thought.20

  • Avoiding Epistemic Injustice and Homogenization: The BKC ontology must be designed to avoid imposing a single, dominant (typically Western) worldview. Instead, it should be capable of respectfully accommodating, representing, and navigating these diverse ontologies. This may involve employing advanced knowledge representation techniques such as “standpoint logic” or other frameworks designed for multi-perspective knowledge representation, allowing different ontological views to coexist and be understood in relation to each other without forcing them into a single, potentially distorting, structure.28

  • CARE Principles for Indigenous Data Governance: The CARE principles—Collective Benefit, Authority to Control, Responsibility, and Ethics—provide a guiding framework for how Indigenous data should be governed throughout its lifecycle.29 These principles must inform the design of the BKC ontology, particularly how IKS is structured, tagged, accessed, and managed, ensuring that these actions align with Indigenous self-determination and well-being.

The World Intellectual Property Organization (WIPO) and its work on Traditional Knowledge (TK) also provide relevant context. The recently adopted WIPO Treaty on Intellectual Property, Genetic Resources and Associated Traditional Knowledge (GRATK Treaty) aims to combat the misappropriation (biopiracy) of TK and genetic resources by, among other things, requiring disclosure of origin in patent applications. This treaty recognizes the living, evolving nature of TK and includes specific provisions for Indigenous Peoples and local communities.30 This international legal framework reinforces the importance of protecting IKS and is a pertinent consideration for how such knowledge is handled and attributed within the BKC, particularly if it relates to genetic resources or has potential for commercial application.

Integrating Indigenous Knowledge Systems into the BKC ontology is not a simple task of data mapping; it is a profound ethical and intellectual undertaking. It demands that the very design of the ontology and its surrounding governance structures be built upon the bedrock of Indigenous Data Sovereignty. This means that IDSov principles26 and ethical frameworks like CARE29 and OCAP®25 are not merely add-ons but are deeply embedded into how IKS is conceptualized, represented, accessed, controlled, and stewarded within the BKC. This has direct implications for the ontological structures themselves—perhaps requiring distinct modules for IKS governed directly by Indigenous communities, the use of Traditional Knowledge (TK) Labels25 to communicate culturally appropriate use, and ensuring that access controls and permissions within the ontology reflect and enforce community-defined protocols. The WIPO GRATK Treaty31 further provides an international legal context supporting such protective measures. Without this foundational commitment to IDSov, any attempt to include IKS risks perpetuating epistemic injustice and undermining the very purpose of a truly inclusive Bioregional Knowledge Commons.

2.4. Advanced Techniques for Ontology Development and Enrichment

To build a robust and dynamic BKC ontology, especially one that aims to integrate diverse and multimedia knowledge, advanced techniques in ontology engineering, artificial intelligence, and knowledge representation will be indispensable. These tools can augment the community-driven “ontology commoning” process, helping to manage complexity, accelerate development, and unlock deeper insights from the collected knowledge.

Leveraging Large Language Models (LLMs):

LLMs have shown significant potential in various stages of ontology engineering:

  • Ontology Requirements Engineering (ORE): LLMs can function as sophisticated assistants to knowledge engineers and domain experts (including community members). They can help in eliciting requirements, translating informal descriptions into more structured specifications, and even generating draft competency questions that the ontology should be able to answer.16 By analyzing community workshop transcripts or discussion forum content, LLMs could help identify recurring themes, key concepts, and relationships that need to be captured in the ontology. The maturity of LLM applications in Software Requirements Engineering (SRE) can provide valuable insights and transferable methodologies for ORE tasks.16

  • Ontology Enrichment and Mapping: Once a foundational ontology exists, LLMs can assist in its enrichment by processing large volumes of textual data relevant to the bioregion (e.g., reports, articles, local histories) to suggest new concepts, attributes, relationships, or instances. They can also aid in ontology mapping by identifying semantic similarities between the BKC ontology and external ontologies or vocabularies, facilitating interoperability.32 Toolkits like OntoAligner explicitly incorporate LLMs for ontology alignment tasks, offering functionalities beyond traditional methods.32

  • Hybrid Approaches (LLMs + Knowledge Organization Systems): A promising direction is the combination of LLMs with established Knowledge Organization Systems (KOS) like ontologies and taxonomies. This hybrid approach aims to balance the generative and natural language understanding capabilities of LLMs with the precision and defined terminology of KOS.33 For the BKC, this could mean using the ontology to provide structured context to an LLM, thereby improving the relevance and factual accuracy of LLM-generated summaries or answers related to bioregional topics.

Ontology-Grounded Retrieval Augmented Generation (OG-RAG):

This is a particularly relevant advanced technique for the BKC:

  • Concept: OG-RAG is designed to enhance the responses of LLMs by anchoring their retrieval processes firmly within domain-specific ontologies.34 The core idea is to construct a hypergraph representation of the domain documents (or knowledge base content), where each hyperedge encapsulates a cluster of factual knowledge that is explicitly grounded in and structured by the domain ontology. When a query is posed, an optimization algorithm retrieves a minimal yet comprehensive set of these ontology-grounded hyperedges to form a precise and conceptually rich context for the LLM to generate its response.34

  • Application in BKC: OG-RAG can serve as a powerful query interface for the BKC. Users could ask complex questions in natural language (e.g., “What are the traditional fire management practices of the [Indigenous Group Name] in the of our bioregion, and how do they compare to current agency protocols considering projected drought conditions?”). OG-RAG would then leverage the BKC ontology to retrieve the most relevant, factually accurate, and interconnected pieces of information from diverse sources (IKS, scientific data, policy documents) to provide a synthesized, context-aware, and verifiable answer. This is especially valuable for navigating the complex, multi-faceted knowledge inherent in a bioregion.

Knowledge Graph (KG) Integration:

The BKC ontology will serve as the semantic schema or blueprint for the BKC knowledge graph:

  • KGs represent information as a network of entities (nodes) and their relationships (edges).35 The BKC ontology will define the types of entities (e.g., species, ecosystems, cultural practices, community projects, knowledge resources) and the types of relationships (e.g., ‘is_located_in’, ‘is_practiced_by’, ‘monitors_impact_on’) that can exist within the BKC knowledge graph.

  • KGs are adept at integrating diverse data from both structured (e.g., databases, spreadsheets) and unstructured (e.g., text documents, web pages) sources.36 This capability is crucial for the BKC, which will need to incorporate heterogeneous bioregional data.

  • LLMs can also play a role in KG construction and completion, for instance, by extracting entities and relations from text to populate the KG, or by predicting missing links based on existing patterns.37

Graph Neural Networks (GNNs):

GNNs are a powerful class of deep learning models specifically designed to operate on graph-structured data. Unlike traditional embedding methods that might rely on random walks (like RDF2Vec or OWL2Vec*), GNNs learn node representations by iteratively aggregating information from a node’s neighbors through a process called “message passing.” This allows them to capture complex structural and semantic patterns within the graph.

GNNs offer significant potential for the BKC ontology:

  • Enhanced Ontology Alignment and Matching: GNNs can learn more sophisticated embeddings that capture not just direct relationships but also higher-order structural similarities between concepts across different ontologies, leading to more accurate and nuanced alignments.
  • Knowledge Graph Completion and Link Prediction: They excel at predicting missing links or entities within a knowledge graph, crucial for enriching the BKC ontology and its associated KG from diverse data sources.
  • Concept Discovery and Clustering: By learning rich embeddings, GNNs can help identify latent concepts or cluster similar ideas within unstructured text or existing ontological fragments, aiding in the initial stages of ontology development.
  • Narrative Understanding: GNNs could be applied to model the relationships between characters, events, and themes within stories, potentially improving the “Story → Belief System” extraction process by capturing more complex narrative structures.

Ontology Embeddings:

These techniques translate ontological components into dense vector representations in a continuous mathematical space:

  • Methods like EIKE (Extensional and Intensional Knowledge Embedding) aim to capture both the extensional knowledge (about specific instances belonging to concepts) and intensional knowledge (about the inherent properties, characteristics, and semantic associations among concepts) within these embeddings.38

  • These vector representations enable a range of valuable functionalities within the BKC39:

  • Calculating semantic similarity between concepts, entities, or documents.

  • Predicting new or missing links (relationships) within the knowledge graph.

  • Enhancing search and recommendation systems (e.g., suggesting related knowledge resources or community members with relevant expertise).

  • Integrating the ontological knowledge with machine learning models for more advanced analytical tasks, such as trend prediction or impact assessment within the bioregion.

  • Crucially, these embeddings can also aid in suggesting merges, alignments, or translations between different ontological modules, supporting the principles of ontological pluralism.

Ethical Considerations for AI in Ontology Development:

The application of AI tools like LLMs and GNNs presents a dual aspect for BKC ontology development. On one hand, they are powerful enablers, offering capabilities to accelerate development, manage complexity, and enrich the ontology from diverse data sources.16 On the other hand, their uncritical application carries risks. LLMs and GNNs are typically trained on vast, general-domain datasets which may embed dominant (e.g., Western, Anglophone) perspectives and ontological assumptions. If these tools are used without careful guidance and robust community validation, they could inadvertently homogenize the diverse, local, and Indigenous ontologies that the BKC aims to respect and integrate.20 There is a danger that AI might streamline or translate unique knowledge forms into a pre-existing or AI-generated dominant structure, thereby undermining the principles of ontological pluralism and the participatory goals of “ontology commoning.” Therefore, the role of AI in BKC ontology development must be carefully circumscribed: it should serve as a tool in support of community-led commoning and pluralism. Its use should be focused on tasks like initial drafting of ontological elements, identifying patterns in community discussions for further human review, or managing the complexity of large knowledge bases, always remaining subject to rigorous human oversight and validation from the diverse stakeholders of the bioregion.

2.5. Embracing Ontological Pluralism within the BKC Framework

A foundational principle for the BKC ontology, especially given its commitment to integrating diverse knowledge systems including IKS, is the explicit embrace of ontological pluralism. Ontological pluralism acknowledges that there can be multiple, coexisting, and valid ways of understanding, categorizing, and representing reality.40 It recognizes that different cultures, communities, or disciplines may operate with different fundamental assumptions about what entities exist, what their natures are, and how they relate to one another. These differing “maps” of reality are not necessarily right or wrong in an absolute sense, but rather reflect different perspectives, experiences, and purposes.

The relevance of ontological pluralism to the BKC is profound. It is crucial for respectfully and meaningfully integrating diverse knowledge systems, particularly when bringing together Western scientific ontologies (which often assume a single, objective material world) and various Indigenous ontologies. Indigenous worldviews may posit fundamentally different understandings of nature, agency, causality, and the relationships between humans, non-humans, and the spiritual realm.41 For instance, as noted earlier, the Stó:lō understanding of an ancestral stone as a living being27 or Amazonian concepts of multinaturalism20 cannot be easily reconciled with, or reduced to, standard Western scientific categories without significant loss of meaning or epistemic violence. A BKC that attempts to force all knowledge into a single, monolithic ontological framework risks marginalizing or distorting these vital perspectives.

To effectively support ontological pluralism, the BKC framework can employ several strategies:

  • Avoiding a Single “Master” Ontology: Instead of striving for one overarching ontology that attempts to encompass all knowledge, the BKC ontology could be conceptualized and designed as a network of interconnected, yet potentially distinct, ontological modules or “standpoints”.28 Each module could represent a particular knowledge system (e.g., a specific Indigenous tradition, a local ecological understanding, a scientific discipline) with its own internal coherence and categories.

  • Employing Standpoint Logic or Multi-Perspective Knowledge Representation Frameworks: Technical approaches like standpoint logic offer formalisms for expressing information relative to different standpoints or worldviews.28 Such frameworks aim to preserve the integrity and entailments of each perspective while enabling interoperability, comparison, or translation where appropriate and meaningful. This allows the BKC to represent potentially conflicting or incommensurable claims or categorizations without forcing a premature or artificial resolution.

  • Ontological Translation for Interoperability: While avoiding a single master ontology, the BKC will actively enable interoperability and translation between different ontological standpoints. This involves developing mechanisms to bridge diverse conceptual models, allowing communities to understand and work with varied perspectives without requiring full semantic alignment or homogenization. Inspired by principles from Cambria42 (a framework for data transformation and schema evolution), this approach emphasizes “lenses” and “transformations” to facilitate data flow and understanding across different representations. AI, particularly using GNNs and advanced embedding techniques, can play a crucial role in proposing these translations, which are then subject to rigorous community validation and refinement.

  • Explicit Provenance and Contextualization: It is vital that all knowledge within the BKC is clearly attributed to its source and its cultural or epistemic context.35 The ontology and its associated interfaces should allow users to understand the ontological lens through which specific pieces of information are presented. This includes making transparent the assumptions and definitions underlying different parts of the knowledge base.

  • Community-Driven Mapping and Alignment: Rather than imposing top-down alignments or mappings between different ontological modules, the BKC should facilitate community-led processes for identifying correspondences, differences, and potential points of dialogue or translation between them.21 Tools like OntoAligner32 could support such processes if utilized in a participatory and culturally sensitive manner, guided by the principle that alignment should emerge from community understanding rather than purely algorithmic similarity.

By consciously designing for ontological pluralism, the BKC can become a richer, more inclusive, and more intellectually honest space, capable of holding and honoring the multiplicity of ways in which the bioregion is known, understood, and valued by its diverse inhabitants.

The following tables summarize key considerations for participatory ontology development and Indigenous knowledge integration:

Table 2.1: Comparative Analysis of Participatory Ontology Development Methodologies

Methodology NameCore PrinciplesKey Actors & RolesEngagement TechniquesStrengthsWeaknesses/ChallengesApplicability to BKC
ACCIO Project Methodology 17User-centered design, co-creation, iterative development, grounded in real-world practices.Social Scientists (facilitation, context understanding), Ontology Engineers (technical expertise, formalization), Stakeholders (domain experts, users, e.g., community members, Indigenous representatives - provide input, validate, co-design).Hands-on workshops, observations, interviews, mind maps, sunny-day scenarios, personas, role-play co-design, decision-tree co-design.Balances expert input with deep stakeholder involvement across lifecycle; practical techniques for engagement; produces contextually relevant ontologies.Can be time-intensive for facilitators and stakeholders; eliciting “out-of-the-box” thinking can be challenging.Highly applicable due to its structured yet participatory nature, and clear methods for integrating diverse stakeholder input, crucial for BKC’s inclusive goals.
Human-Centered Ontology Engineering (HCOME) 17Active participation of domain experts throughout the ontology lifecycle; focus on user needs and context.Domain Experts (central role in defining concepts, relationships, validation), Ontology Engineers (facilitate, formalize).Varies, but generally includes interviews, workshops, user studies, prototyping.Ensures ontology is relevant and usable by intended users; promotes ownership by domain experts.May require significant time commitment from domain experts; effectiveness depends on facilitation skills.Applicable, especially its emphasis on domain expert (community) involvement. Needs to be augmented with specific techniques for diverse, non-technical stakeholders.
Community-driven (e.g., Ontokiwi-style) 31Open collaboration, distributed contribution, leveraging familiar collaborative platforms (wiki-like), emergent consensus.Broad Community Members (contributors, editors, reviewers), Facilitators/Moderators (guide process, resolve conflicts), Technical Support.Wiki-based editing, discussion forums, version control, community annotation, voting/rating mechanisms.Potentially highly scalable; leverages collective intelligence; fosters strong community ownership; lowers barrier to contribution for some.Maintaining quality and consistency can be challenging; susceptible to vandalism or dominance by certain voices if not well-moderated; may not suit all types of knowledge (e.g., sacred/restricted IKS).Partially applicable. Excellent for certain types of knowledge and broad community input. Would need careful integration with more structured processes for core ontology elements and sensitive IKS, ensuring appropriate protocols are followed.
Ontology Commoning (as per Hylo) 29Ongoing organic growth of common ontology, co-creation, interoperability focus, supporting ever-wider collaboration, process-oriented rather than fixed result.Developers, Users of tools/platforms, Facilitators.Online sessions, dialogue, development of shared methodologies, knowledge base creation.Focuses on evolving understanding and interoperability between systems/communities; embraces complexity and emergence.Still a developing concept; requires commitment to ongoing dialogue and adaptation; may be abstract for some community members.Highly aligned with BKC’s philosophical goals. Provides a guiding framework for the process of ontology development, emphasizing iterative co-creation and adaptability, which can incorporate specific methods from ACCIO or HCOME.

Table 2.2: Principles and Protocols for Ethical Indigenous Knowledge Integration in the BKC Ontology

Principle/ProtocolDescriptionImplications for BKC Ontology DesignImplications for BKC Governance
Free, Prior, and Informed Consent (FPIC) 35Indigenous communities must give explicit, uncoerced consent before any knowledge is shared, based on full understanding of its use.Ontology must have mechanisms to track consent for specific knowledge elements. Representation of IKS should only occur after FPIC.Governance processes must mandate and document FPIC for all IKS integration. Clear procedures for seeking and managing consent.
OCAP® (Ownership, Control, Access, Possession) 35Indigenous communities own their information, control its collection/management, must be able to access it, and have (or determine stewardship of) possession.Ontology design must support attribution of ownership to communities. Access control mechanisms within the ontology must reflect community-defined rules. Metadata fields for OCAP® status.Governance must affirm and enact OCAP® principles. Indigenous communities have final say on their data within the BKC. Data stewardship models co-designed.
CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) 43Data use must benefit Indigenous Peoples; they must have authority to control it; those handling data are responsible for its ethical use and supporting self-determination; Indigenous rights and well-being are paramount.Ontology should facilitate tracking of benefits. Semantic structures for IKS should be co-designed with Indigenous communities to reflect their authority. Metadata for ethical use guidelines.Governance must ensure all BKC activities involving IKS adhere to CARE. Indigenous representation in decision-making bodies. Accountability mechanisms.
Indigenous Data Sovereignty (IDSov) 40The inherent right of Indigenous Peoples to govern their data throughout its lifecycle.The ontology must be a tool for IDSov, not a constraint on it. This may mean separate, Indigenous-governed ontological spaces for IKS, linked but not subsumed by a general ontology. Use of TK Labels (e.g., Local Contexts 35) to embed protocols.IDSov is a non-negotiable foundation of BKC governance. Indigenous communities determine how their knowledge is represented, shared, and used within the BKC.
Respectful Representation & Contextual Integrity 35IKS must be represented accurately, respectfully, and within its cultural/spiritual context. Avoid decontextualization or forcing into alien frameworks.Ontology must allow for rich contextual metadata. Support for Indigenous languages and terminologies. Flexible structures that can accommodate non-Western relational models and concepts (ontological pluralism).Governance includes processes for community review and validation of IKS representations. Training for non-Indigenous contributors on respectful engagement.
Reciprocity & Benefit Sharing 35Fair compensation for knowledge sharing and ensuring tangible benefits flow back to Indigenous communities.Ontology could potentially link to mechanisms for benefit sharing or tracking contributions.Governance models should include clear policies on compensation and benefit sharing, co-designed with communities.

Section 3: Semi-Automating the Story-to-Ontology Pipeline with AI

Building upon the principles of “ontology commoning” and the crucial role of participatory design, this section introduces a vision for semi-automating the pipeline from natural language stories to formal ontologies using Artificial Intelligence. This approach aims to augment human efforts in meaning-making, not replace them, by providing tools that scaffold the collective movement from fragmented narratives toward shared understanding. This aligns with the “Regenerating Narrative through Ontological Commoning” concept43 and the “90-minute exercise in Ontological Commoning” methodology44.

The pipeline can be broken into modular steps that combine:

  • Natural Language Understanding
  • Symbolic Representation (Knowledge Graphs, Ontologies)
  • Interactive Dialogue and Iteration

3.1. Step-by-Step: How to Semi-Automate the Process

1. Story → Belief System

  • Input: Natural language stories (spoken or written)
  • Output: Propositional beliefs and assumptions
  • Approach:
    • Use LLMs (e.g., GPT-4, Claude) to extract:
      • Core narrative arcs
      • Implicit assumptions (e.g., “Community makes me feel safe”)
      • Moral or causal relationships (e.g., “Because people shared chores, I felt respected”)
    • Tools like semantic role labeling, frame semantics, or narrative event chain extraction can augment this.
    • Optionally use Socratic prompting:
      • “What must the storyteller believe for this story to make sense?”
      • “What values or principles are implicit here?”
    • Output format: beliefs as proposition(subject, predicate, object) or natural-language summaries, stored in a structured format (e.g., JSON).

2. Belief System → Ontology

  • Input: List of beliefs or propositions
  • Output: An explicit ontology — i.e., a set of entities, concepts, and relationships
  • Approach:
    • Cluster beliefs by semantic domain (e.g., self, community, governance, nature)
    • Use LLMs to suggest ontology terms:
      • Entities: “Person”, “Community”, “Chore”
      • Relationships: participatesIn, resolves, leadsTo
    • Use AI tools to formalize into RDF/OWL:
      • LLM + templating = initial draft of ontology in Turtle or JSON-LD
      • Use OntoGPT, Prompt2Schema, or custom fine-tuned models
    • For alignment and validation:
      • Use existing vocabularies (e.g., Schema.org, FOAF, PATO)
      • Use embedding-based ontology alignment or tools like LODE, ROBOT, or OntoRefine

3. Dialogue Support for Co-Ontology Formation

  • Goal: Once individual ontologies are created, help groups compare and map them, and actively facilitate the commoning process.
  • Approach: AI can serve as a sophisticated facilitator in this crucial stage, moving beyond mere data processing to actively support human dialogue and negotiation.
  • Tools & Techniques:
    • AI as a Dialogue Facilitator: AI agents can analyze real-time discussions (from transcripts or direct input) to:
      • Highlight Discrepancies and Commonalities: Automatically surface areas where different participants use varying terminology, hold implicit assumptions, or express conceptual conflicts. This helps focus human attention on critical points of divergence.
      • Suggest Bridging Concepts and Translations: Propose potential “bridging” terms, relationships, or even “ontological translations” (inspired by frameworks like Cambria42) that might help reconcile disparate views without forcing homogenization.
      • Summarize and Synthesize: Generate concise summaries of agreements, unresolved issues, and proposed next steps, ensuring collective understanding is accurately captured and accessible.
      • Track Evolution: Monitor the evolving ontology and identify potential inconsistencies introduced by new contributions, suggesting refinements for human review.
    • Vector Embeddings of Ontologies: Utilize advanced ontology embeddings (including those generated by GNNs) for calculating semantic similarity between concepts, entities, or documents. This powers the AI’s ability to identify related terms and potential alignments.
    • Visual Tools: Employ tools like WebVOWL, OntoGrapher, or custom GraphUI to visually represent the evolving ontologies and their relationships, making complex semantic structures more accessible for community review and interaction.

3.2. Concrete AI Tools to Use

TaskTool/Tech
Story transcription & summarizationWhisper, GPT
Belief extractionGPT + Socratic prompts
Ontology generationOntoGPT, RDFLib, SPARQLTools, llama-index with KG plugins
Graph embedding & alignmentRDF2Vec, OWL2Vec*, AmpliGraph, GNN frameworks (e.g., PyTorch Geometric, DGL, TensorFlow GNN)
Interactive UIStreamlit, Gradio, Jupyter + D3.js for graph vis
Persistent storageTerminusDB, Neo4j, GraphDB, or RDF triple store (e.g., Blazegraph)

3.3. How to Start a Prototype Project

  • Small pilot: Start with 3–5 people sharing real stories in a shared context (e.g., co-living, ecological collaboration).
  • Human-in-the-loop AI: Use AI to generate belief systems and ontologies, then refine via human review.
  • Store and visualize the evolving personal and shared ontologies in a triple store with graph UI.
  • Observe emergence of shared terms, contested terms, and consensus zones.

This project could become the basis of an Ontology Commons Toolkit — blending AI, participatory design, and meaning-making in a fragmented world.

3.4. Closing Thought

Regenerating narratives requires deep listening. AI, properly designed, can help hold the space — not by replacing human meaning-making, but by scaffolding our collective movement from fragmentation toward shared understanding.

Conclusion

The foundation of a successful Bioregional Knowledge Commons rests on two interrelated pillars: a clear understanding of bioregions as integrated “life-places” where ecological and cultural systems are inseparable, and a commitment to “ontology commoning” as the participatory process through which communities co-create the semantic structures for their collective knowledge.

The integration of Indigenous Knowledge Systems through principles of data sovereignty, the embrace of ontological pluralism, and the strategic use of AI tools as servants rather than masters of the commoning process all point toward a new paradigm for place-based knowledge stewardship. This is not merely about building a database or deploying new technologies; it is about fostering a living system where knowledge creation, sharing, and application become regenerative forces within the bioregion.

In Part 2, we will explore how these conceptual foundations translate into technical architecture, examining decentralized technologies, knowledge representation methods, and user interfaces that can embody these principles. The journey from vision to implementation requires careful attention to both the technical and social dimensions of this transformative project.

Continue to Part 2: Technical Architecture for Sovereignty and Engagement →

References

Footnotes

  1. Bioregionalism - Wikipedia, accessed May 21, 2025, https://en.wikipedia.org/wiki/Bioregionalism ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16

  2. What is a bioregion? • Bioregional Learning Centre, accessed May 21, 2025, https://www.bioregion.org.uk/blog-posts/what-is-a-bioregion ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  3. (PDF) Humanity’s Bioregional Places: Linking Space, Aesthetics, and the Ethics of Reinhabitation - ResearchGate, accessed May 21, 2025, https://www.researchgate.net/publication/276031996_Humanity’s_Bioregional_Places_Linking_Space_Aesthetics_and_the_Ethics_of_Reinhabitation ↩

  4. Bioregioning; Design, Ecology and a future - CIRCULAR DESIGN WEEK 2024, accessed May 21, 2025, https://cdw.re-public.jp/2024/archive/conference-01/ ↩ ↩2 ↩3

  5. Bioregionalism - P2P Foundation, accessed May 21, 2025, https://wiki.p2pfoundation.net/Bioregionalism ↩ ↩2 ↩3 ↩4

  6. Where are you at? Re‐engaging bioregional ideas and what they offer geography, accessed May 21, 2025, https://eprints.whiterose.ac.uk/id/eprint/202190/1/Geography%20Compass%20-%202023%20-%20Hubbard.pdf ↩ ↩2 ↩3

  7. “Governing Knowledge Commons — Introduction & Chapter 1” by …, accessed May 21, 2025, https://scholarship.law.pitt.edu/fac_book-chapters/8/ ↩ ↩2 ↩3 ↩4 ↩5

  8. www.unesco.org, accessed May 21, 2025, https://www.unesco.org/en/articles/knowledge-commons-and-enclosures#:~:text=%22The%20knowledge%20commons%20specify%20that,making%20the%20futures%20they%20imagine.%22 ↩ ↩2

  9. Knowledge Commons - P2P Foundation, accessed May 21, 2025, https://wiki.p2pfoundation.net/Knowledge_Commons ↩ ↩2

  10. About - P2P Foundation, accessed May 21, 2025, https://p2pfoundation.net/the-p2p-foundation/about-the-p2p-foundation ↩ ↩2

  11. Bioregioning in Practice: Learn from the World’s Leading Practitioners - Gaia Education, accessed May 21, 2025, https://www.gaiaeducation.org/bioregioning-in-practice ↩

  12. About CBBRS - SUNY ESF, accessed May 21, 2025, https://www.esf.edu/cbbrs/about.php ↩

  13. Ontology (information science) - Wikipedia, accessed May 21, 2025, https://en.wikipedia.org/wiki/Ontology_(information_science) ↩ ↩2 ↩3

  14. Principles and tools for developing standardized and interoperable ontologies - MIDAS, accessed May 21, 2025, https://midas.umich.edu/publication/principles-and-tools-for-developing-standardized-and-interoperable-ontologies/ ↩

  15. www.scitepress.org, accessed May 21, 2025, https://www.scitepress.org/Papers/2011/36547/36547.pdf ↩ ↩2 ↩3 ↩4

  16. Leveraging Large Language Models for Ontology Requirements Engineering - King’s Research Portal, accessed May 21, 2025, https://kclpure.kcl.ac.uk/portal/files/332879450/Leveraging_Large_Language_Models_for_Ontology_Requirements_Engineering.pdf ↩ ↩2 ↩3 ↩4

  17. Ontological Analysis to understand the Interplay between Ecosystem Services, Human Well-being, and Climate Change - Current World Environment, accessed May 21, 2025, https://cwejournal.org/vol2no2/pontological-analysis-to-understand-the-interplay-between-ecosystem-services-human-well-being-and-climate-changep ↩ ↩2

  18. hbabaie1/Sustainable-Development-and-Climate-SDC … - GitHub, accessed May 21, 2025, https://github.com/hbabaie1/Sustainable-Development-and-Climate-SDC-ontology ↩ ↩2 ↩3

  19. (PDF) An Ontology-based Model for Urban Planning Communication - ResearchGate, accessed May 21, 2025, https://www.researchgate.net/publication/225393554_An_Ontology-based_Model_for_Urban_Planning_Communication ↩

  20. Indigenous Knowledge and Ontological Difference? Ontological Pluralism, Secular Public Reason, and Knowledge between Indigenous Amazonia and the West | Comparative Studies in Society and History - Cambridge University Press, accessed May 21, 2025, https://www.cambridge.org/core/journals/comparative-studies-in-society-and-history/article/indigenous-knowledge-and-ontological-difference-ontological-pluralism-secular-public-reason-and-knowledge-between-indigenous-amazonia-and-the-west/B8612F5B5BEA89012C2140AFB6F78C5B ↩ ↩2 ↩3 ↩4 ↩5

  21. Ontological commoning to support collaboration | Hylo, accessed May 21, 2025, https://www.hylo.com/groups/collaborative-technology-alliance/post/56678 ↩ ↩2 ↩3

  22. Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia - PMC, accessed May 21, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC5001762/ ↩

  23. Collaborative ontology development | PPT - SlideShare, accessed May 21, 2025, https://www.slideshare.net/slideshow/noy-collaborative-ontology-development/24262881 ↩

  24. Traditional knowledge | UNESCO UIS, accessed May 21, 2025, https://uis.unesco.org/en/glossary-term/traditional-knowledge ↩ ↩2

  25. Beyond Conservation: Working Respectfully with Indigenous People …, accessed May 21, 2025, https://ipcaknowledgebasket.ca/resources/working-respectfully-with-indigenous-people-and-their-knowledge-systems/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9

  26. Indigenous Data Sovereignty - Research at UCalgary - University of Calgary, accessed May 21, 2025, https://research.ucalgary.ca/engage-research/indigenous-research-support-team/irst-resources/indigenous-data-sovereignty ↩ ↩2

  27. What Are Indigenous Ontologies? - BGC Exhibitions, accessed May 21, 2025, https://exhibitions.bgc.bard.edu/cam/files/2022/03/Aaron-Glass_What-Are-Indigenous-Ontologies.pdf ↩ ↩2

  28. Standpoint Logic: Multi-Perspective Knowledge Representation, accessed May 21, 2025, https://iccl.inf.tu-dresden.de/w/images/f/f4/FAIA-344-FAIA210367.pdf ↩ ↩2 ↩3

  29. CARE Statement for Indigenous Data Sovereignty - the United Nations, accessed May 21, 2025, https://www.un.org/digital-emerging-technologies/sites/www.un.org.techenvoy/files/GDC-submission_WAMPUM_Lab_and_the_Collaboratory_for_Indigenous.pdf ↩ ↩2

  30. Traditional Knowledge and Intellectual Property - WIPO, accessed May 21, 2025, https://www.wipo.int/edocs/pubdocs/en/wipo-pub-rn2023-5-1-en-traditional-knowledge-and-intellectual-property.pdf ↩

  31. WIPO Treaty on Intellectual Property, Genetic Resources and …, accessed May 21, 2025, https://en.wikipedia.org/wiki/WIPO_Treaty_on_Intellectual_Property,_Genetic_Resources_and_Associated_Traditional_Knowledge ↩

  32. OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment, accessed May 21, 2025, https://arxiv.org/html/2503.21902v1 ↩ ↩2 ↩3

  33. Chatting with Papers: A Hybrid Approach Using LLMs and Knowledge Graphs - arXiv, accessed May 21, 2025, https://arxiv.org/html/2505.11633v1 ↩

  34. OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models - arXiv, accessed May 21, 2025, https://arxiv.org/html/2412.15235v1 ↩ ↩2

  35. Forming a Knowledge Commons for Earth and Space Sciences: Lessons From Past Efforts, accessed May 21, 2025, https://knowledgestructure.pubpub.org/pub/narockandprabhu ↩ ↩2

  36. Seamlessly Link Structured and Unstructured Data with a Knowledge Graph - Shelf.io, accessed May 21, 2025, https://shelf.io/blog/link-structured-and-unstructured-data-with-knowledge-graph/ ↩

  37. Integrating Large Language Models and Knowledge Graphs for Next-level AGI - Emory Computer Science, accessed May 21, 2025, https://www.cs.emory.edu/~jyang71/files/klm-tutorial.pdf ↩

  38. Embedding Ontologies via Incorporating Extensional and Intensional Knowledge - SciEngine, accessed May 21, 2025, https://www.sciengine.com/doi/10.3724/2096-7004.di.2024.0088 ↩

  39. Ontology Embedding: A Survey of Methods, Applications and Resources - arXiv, accessed May 21, 2025, https://arxiv.org/pdf/2406.10964 ↩

  40. (PDF) Ontological pluralism and social values - ResearchGate, accessed May 21, 2025, https://www.researchgate.net/publication/378900214_Ontological_pluralism_and_social_values ↩

  41. Diverse values of nature and political ontology - Ecology & Society, accessed May 21, 2025, https://ecologyandsociety.org/vol30/iss2/art13/ ↩

  42. Ink & Switch, “Cambria: A Data Transformation and Schema Evolution Framework,” Ink & Switch, accessed May 27, 2025, https://www.inkandswitch.com/cambria/ ↩ ↩2

  43. Simon Grant, “Imagining New Stories,” Simon Grant’s Wiki, March 17, 2025, https://wiki.simongrant.org/doku.php/d:2025-03-17 ↩

  44. Simon Grant, “90-minute exercise in Ontological Commoning,” Simon Grant’s Wiki, May 20, 2025, https://wiki.simongrant.org/doku.php/o:90-minute_exercise ↩

Graph View

Backlinks