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The need for a Space Data Knowledge Commons

As our society expands technologically and physically into space, our flourishing is inextricable from the space environment. However, our data and knowledge infrastructure has not kept pace with this rapid change.

Published onAug 16, 2021
The need for a Space Data Knowledge Commons


“If large portions of the world remain unseen or inaccessible to us, we must consider the meaning of the word ‘reality’ with great care.”

— Marcelo Gleiser, The Island of Knowledge


2021 has marked a new vista in space exploration, seeing Blue Origin and Virgin Galactic launching travelers into space, and the launch of NASA’s Artemis program.

Our technological and physical expansion into space exemplifies the increasing interconnections between Earth and the space environment. However, although our flourishing increasingly depends on our integration with broader horizons, our data and knowledge infrastructure have remained essentially static for decades, and are no longer sufficient to meet our increasingly dynamic information requirements.

The problem of our outdated data systems is not one of information, but of access. Datasets, disciplines, people, projects, institutions are all siloed, resulting in a lack of awareness and usability across silos that make reuse and collective progress impossible. Yet our increasing awareness of complexity has revealed that the distinctions between the silos are artificial, with each new bit of information further revealing the interconnectedness that pervades our world. As John Muir observed, “When we try to pick out anything by itself, we find it hitched to everything else in the universe.” 

In pursuit of a mechanism for delivering sufficiently complex data, calls for knowledge systems have emerged across the space community (e.g. [1]; [2]), along with some proposed solutions (e.g., https://www.consensys.space/, http://sites.utexas.edu/moriba/astriagraph/).

In this paper, we build on these ideas and focus on the further steps needed to produce a flourishing space data community. We acknowledge that asymmetries in knowledge lead to unhealthy communities, and offer a framework to address the asymmetries: a knowledge commons.

A Knowledge Commons for the Space Data Community

A knowledge commons is a combination of intelligent information representation and the openness, governance, and trust required to create a participatory ecosystem whereby the whole community maintains and evolves this shared information space.  A knowledge commons is predicated on a central movement from a data society to a knowledge and wisdom society.

There are key progressions from data to information to knowledge to wisdom. A helpful way to understand the progression is through the metaphors of the city and language:


Data

Information

Knowledge

Wisdom

Distinguishing Characteristic

Bits

Combinations

Interconnections

Understanding and adaptation (the commons)

City

Bricks

Buildings

Infrastructure connecting buildings (e.g., roads)

Humans navigating and building systems on the connected infrastructure

Language

Letters

Words

Sentences and paragraphs

A great novel


In the following sections, we offer a vision for how an improved knowledge system can progress a space data landscape, across these gradations: 

  • How can we better understand the resources (people, capabilities, assets, contents, data, models) available about outer space? 

  • To what extent is cohesion the key to a more flourishing community?

This development should be considered a living document, intended to be a seed for the convergent community capable of sustaining the commons;  we encourage the community to participate in emerging answers and further questions raised in this piece and identify areas of exploration in the last section of this paper.

The Need for a Space Data Knowledge Commons

A commons is any unregulated resource open to all, most traditionally associated with the atmosphere, oceans, and unclaimed territory such as Antarctica. However, in the age of space exploration/exploitation and information, we need to think of commons more capaciously, including not only physical outer space, but also the knowledge spaces of society such as the internet and data archives. 

Most information infrastructures fall short of the ideal of ‘open access,’ being restrictive either in the tools or know-how required to utilize them. The outcome is a fragmentation of the information available, an impoverished lens on the system being studied, and imbalances of knowledge. Asymmetries in information/knowledge access create asymmetries and imbalances that reach across a community. 

A knowledge commons is a core ‘technology’ (defined to include both hardware/software and cultural technologies) of the solution for a more inclusive, open, and equitable space community. In this participatory ecosystem, the whole community maintains and evolves the shared space. We believe that the path towards creating this commons lies in an embrace of radical collaboration, new scales of interaction, and the corresponding changes (in thinking, in community structure, and in support) that must accompany this movement [2].

The knowledge commons is the key to a flourishing space community. Just a few priorities that would be aided by the knowledge commons include: 

  • Identifying where information asymmetries and bottlenecks exist;

  • Better recognizing the whole of an individual’s, group’s, or project’s contributions-- information needed to design more productive incentive structures for our community;

  • Understanding where impact is being created to guide more effective provisioning of resources; and 

  • Enable open science [3] in space.

What Is A Knowledge Commons? 

A knowledge commons includes 1) a technical knowledge representation system and a 2) a social community system for producing and governing the knowledge network. Below, we describe examples of each of these systems, building on the following elements:

  • Knowledge Graph (KG): information structured into a graph form by a specific data model/schema/ontology that defines entities (objects, events, situations or abstract concepts) and their relationships. It is a collection of interlinked descriptions of entities – objects, events or concepts. An example is DBpedia [4]

  • Knowledge Network (KN): Connected knowledge graphs. Knowledge networks construct linkages between disparate knowledge bases. An example is the Linked Open Data Cloud (https://lod-cloud.net/). 

  • Knowledge Community: The knowledge network provided in a manner to build community around it--connecting the traditional technological to a cultural technological component. An example is the full complement of Wikimedia Foundation projects and chapters:  (https://www.wikimedia.org/).

The Knowledge Commons (KC) is a combination of these three pieces.

Knowledge Graphs and Networks

Traditionally, data and projects have been organized into independent categories. Consider how information in a spreadsheet is organized: individual data is placed into columns with specific headings. Although explicit data is recorded sufficiently, relationships between columns are left undefined, limiting the ability to move from one to the other. Yet, relationships are perhaps the most important component of data integration and knowledge creation [5]

The emerging graph structure is a solution capable of representing the complexity of our data and of our community. A graph is a collection of concepts and the relational connections between them. In space data, graph representations are able to link observational and simulation data as well as ‘community data’ from our publications, presentations, and other artifacts of projects’ work. We call this a knowledge graph (KG). A well-known example of a KG is DBpedia, a collection of 400 million facts describing 3.7 million things, obtained from Wikipedia--all of which are themselves interlinked, as is the page structure on the Wikipedia website. 

Although graph databases are a relatively new development in computer science, learning theory has indeed traced human cognition as a progression from disconnected concepts to a graph organization. Susan Ambrose from How Learning Works visualizes the progress [6]

The progression of knowledge organization
[from How Learning Works]


The knowledge graph is the first layer of the technical composition of the knowledge commons. Multiple knowledge graphs are then organized into a knowledge network (KN). Knowledge networks construct linkages between disparate knowledge bases. An example is the Linked Open Data Cloud (https://lod-cloud.net/).

From Knowledge Graphs and Networks to A Knowledge Commons

In itself, the knowledge network will break down technical/disciplinary silos and create a system for more effective data sharing and collective understanding. However, for a flourishing space data community to develop around the knowledge network, there must also be a platform (or multiple interoperable platforms), enabling a stewarding community to access the network, enrich it with their knowledge, and connect with one another [7].

Just as Wikipedia provides a platform for “every single human being [to] freely share in the sum of all knowledge” and to shape that knowledge as it evolves over time [Wikimedia Foundation vision statement; https://wikimediafoundation.org/our-work/], those in the space community need to design governance strategies for their own knowledge network. Governance addresses both the aspirational (‘who do we want to be?’) and mundane (‘who can do what?’) [8]

Elements of a governance strategy include:

  • Explicit vision and objectives

  • Clearly defined roles and responsibilities

  • Design to maintain agility and flexibility

  • Large-scale strategy with room for local-scale experimentation

These elements reveal a number of challenges that governance must confront, including: data access, maintaining and evolving ontologies, interoperability of ontologies, multi-level coordination and collaboration, managing risk, engaging the public, transparency and accountability, and trust. Discussions for how the Community can be engaged to emerge these answers is described in the following section.

Effective governance requires participation by the entire community and the entire community (across demographic, disciplinary, national, and public-private sector boundaries) must participate for a flourishing space data knowledge commons. The burgeoning field of Open Science offers a framework for this. Open Science objectifies increased rigour, accountability, and reproducibility for research and is based on the principles of inclusion, fairness, equity, and sharing [9]. Open Science spans all disciplines and is the set of tools and know-how for the modern research environment.

Through the integration of these technical information and collaboration platforms, the knowledge commons will transform space knowledge from a resource held and controlled by few into a shared socio-ecological system [10].

Next Steps

As we contemplate next steps, it’s prudent to reflect on why we have not yet produced such a knowledge commons for space data. The issue is that creating a knowledge network is difficult. The needs are not just technological but social, too, requiring special and uncommon attention being given to governance and trust. As a Community, we must come together to develop methodologies for developing the new structures that this interlocking structure of organization requires. Below we provide an outline for further exploration of the technological and social components of this work.

Building the Knowledge Graph and Network

To create a knowledge commons, we must first create knowledge graphs and networks. The process of creating a knowledge graph/network contains numerous pain points and revealing them is important to communicate to funders and researchers alike where to address their efforts. 

We highlight one example of a project to create a knowledge network with space data and societal connection: The Convergence Hub for the Exploration of Space Science (CHESS) project (CHESSscience.com; [[11]): This National Science Foundation Convergence Accelerator project designed tools to connect disparate data sources via a knowledge network. It set a precedent for domain and ontological scientists to co-develop an ontology (and the technology stack required), and showcased the importance of a user-centered design approach and working with designers to make the product usable by a wide community. 

The CHESS forebear, along with other examples that will emerge, provides a foundation for the development of space data knowledge graphs and networks. However, they will only reach their full potential if they are interoperable and community-driven and if we can identify the core elements and technologies needed to create knowledge graphs and to link them to one another.

An invitation: We encourage the community to emerge a powerful and revealing set of examples.


Trust and Governance & Developing a Community

Much thought is being given to the technologies to create knowledge infrastructure for various pockets of space data, and the next ten years require a development of the trust and governance strategies for that infrastructure to become a flourishing, participatory, adaptable community space [12]

In addition to its initial development, maintaining such a space, whilst avoiding the age old “tragedy of the commons”--whereby users of a shared resource, in the absence of social structures or rules to govern its use, act according to their own self-interest contrary to the good of the community at large, ultimately depleting the resource--requires us to confront the challenges of governance and trust. 

While the field requires additional research, we recognize that governance must confront multiple challenges, including: data access, maintaining and evolving ontologies, interoperability of ontologies, multi-level coordination and collaboration, risk management, public engagement, transparency and accountability, and trust. 

An invitation: We encourage the Community to explore and discuss these issues with us in a discourse around this document and in periodic virtual gatherings (contact author Ryan McGranaghan).



References

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Comments
1
Robert Rovetto: Nice to see some familiar folks. See this project: https://purl.org/space-ontology or https://ontospace.wordpress.com which has been a personal goal to realize since approx. 2011, and is seeking (needing) formal collaborations, sponsors, patrons, and support. It can be interconnected with the topics discussed in this article. Contact at https://ontospace.wordpress.com/contact/