Sensemaking Networks: Project Introduction

I’m excited to start this blog as I begin a new chapter as an Entrepreneur-In-Residence (EIR) at the Astera Institute ! Astera is launching the visionary EIR program to catalyze the upgrading of our aging infrastructure for science publishing and communication.

In this post I'll tell you about my planned residency project, which I'll be developing on along with partners at Astera, Common Sensemakers and beyond (we're still in the process of team formation).

The project is called Sensemaking Networks, an upgrade of social media designed for scientists and other knowledge workers, combining the strengths of AI-augmented social media networks with decentralized semantic nanopublishing, to radically enhance information flow as well as participants’ agency and data sovereignty.

Motivation

Sensemaking Networks are designed to address three interrelated limitations of traditional science publishing and communication:

1. Lack of support for diverse publishing formats and scales. 2. Feedback loops are too long and opaque, and publications’ reach is poor. 3. Non-traditional publications are siloed and fragmented across many apps and formats.

Let's look at each of these in detail:

1. Lack of support for diverse publishing formats and scales. In traditional publishing, the basic unit of knowledge isa paper, a format that has changed surprisingly little over the past hundreds of years, given that since papers, we’ve also invented computers and the internet. Thankfully, in recent years people have been expanding the concept of publishing to include a far wider range of activities, both in terms of formats as well as scale. The Nano publications pro ject is a great example of this nascent trend: as the name implies, nanopublications are aimed at representing very small units of knowledge - typically corresponding to a sentence or two in natural language. What makes nanopublications especially interesting is that they employ Semantic Web principles to represent publications as small knowledge graphs, which are great for representing information in an unambiguous, machine-actionable manner. Nanopublications are also diverse: they can express virtually any kind of knowledge, from a relation between a gene and a disease, to an opinion about a scientific paper or a blog post. More later on why nanopublications are so cool and potentially transformative.

Example nanopublication! ( source ) A nanopublication is like a post, but with semantics (structured fields defining relations to other informational entities).

2. Feedback loops are too long and opaque, and publications’ reach is poor. Traditional publishingis a slow back-and-forth between authors and reviewers, taking months or even years from submission to publication. The review itself is usually hidden from the public, creating a process that is opaque (other researchers in the field could have learned from the review) and non-inclusive (only 3 randomly selected reviewers can participate, why not others?). Finally, traditional methods do little to improve the reach of publications; most research is lost in the deluge of new papers, perhaps never to be found by its intended audience. These shortcomings of traditional publishing help explain why scie nce social media (sh orthand for the use of social media for science communication) has become so popular: scientists can disseminate their research on social media (e.g., Twitter, Bluesky, etc) and receive immediate feedback. Social media’s public nature invites broad participation, and algorithmic feeds coupled with social graphs greatly improve reach: retweets by “science influencers” can lead to millions of views. A key insight we'll be returning to throughout is that, in many ways, researchers are already nanopublishing on social media, but just don’t know it yet. What if Twitter was real academic work?

Chaz Firestone on Twitter / X

and they said twitter wasn't real academic work

3. Non-traditional publications are siloed and fragmented across many apps and formats. While theOpen Science movement has significantly improved access to traditional publications (papers), non-traditional publication is a wild west of non-interoperating apps and formats. As noted above, science social media functions de facto as an important nanopublishing venue where all manner of valuable knowledge is shared. In many cases, commercial platforms like Twitter enclose this data as part of their business model, directly hindering open science. Even on more open social platforms such as Mastodon or Bluesky, the lack of standard formats and support for semantic publishing makes it difficult to effectively aggregate, filter and index data at scale and across platforms.

Enter Sensemaking Networks

We envision Sensemaking Networks as federated/decentralized Twitter-style social networks where posts are nanopublications, and data is open and FAIR (Findable, Accessible, Interoperable and Reusable). As shown in the diagram, this approach aims to draw on the best of social media networks in terms of feedback and reach, while addressing their limitations of siloed data and restricted publishing expressivity.

Sensemaking Networks in the context of current challenges in science publishing

Nanopublications are great for expressing diverse kinds of knowledge, but can be clunky to create and lack the “aliveness” of social networks. By embedding nanopublications in social networks we can enhance their discoverability and encourage their broader adoption by researchers who want to not only publish, but also engage with other researchers and the broader public. AI plays a central role in Sensemaking Networks, both to make semantic publishing as easy as posting on regular social media, and also for improving reach and feedback, for example by connecting people with relevant content and other researchers.

Sensemaking as a missing layer of the scientific record

As we and others have previously made the case for, science social media is far from just an idle diversion for scientists while they aren’t writing papers. Rather, it merits treatment as an essential part of the scientific process: science social media is part of the sensemaking layer of science that helps us to collaboratively communicate, contextualize, curate and assess research. The sensemaking layer improves the inclusivity of science, bringing the broader public into direct contact and participation in discourse around science. This blurring of the lines between scientists and "citizen scientists" is unfortunately part of the reason this layer gets ignored. More generally, we tend to ignore what's hard to measure, and sensemaking is a messy and complex process where knowledge hasn’t yet crystallized into a polished paper:

Ronen Tamari on Twitter / X

While not directly mentioned in the figure, classical publishing and peer review are at the end stages of a nested, iterative multi-scale feedback loop. Information search, filtering, relating concepts, etc are all part of the larger sensemaking process. 3/

And yet, we ignore the sensemaking layer at our peril: Science has not taken ownership of the sensemaking layer, and as a result, it is siloed and fragmented across many platforms , with profiteering publishers vying for control of researchers’ data as part of their transformation into data tracking and analytics conglomerates . These corporations’ operations are often at odds with basic scientific values such as transparency and free access to knowledge. More importantly perhaps, researchers are overwhelmed with information, yet lack access to the people, tools and data that will help them collectively make sense of research and advance science.

Project Plan

We're currently planning two mutually supporting efforts: (1) recovering existing nanopublications from social media (the blue layer above on the left), and (2) building a social app for semantic nanopublishing based on existing open social networking protocols like ActivityPub and AT Protocol (the protocols underlying Mastodon and Bluesky).

🚧The project has a lot of moving parts, so we expect ongoing iteration to improve the plan!

1. Collecting nanopublications from science social media

As discussed above, researchers are already nanopublishing on social media, but because the infrastructure doesn’t actually support it yet, valuable scientific knowledge is being lost through data enclosure and fragmentation. Fortunately, powerful NLP (Natural Language Processing) and text mining tools can help us recover some of this knowledge and make it available on the open scientific record.

This effort will focus on detecting mentions of research in social media posts, linking them back to their authors and inferring their semantics where possible. Recovering semantics from natural language is called parsing in NLP.

For example, consider this tweet on social media:

Melanie Mitchell on Twitter / X

Current chatbots can pass the Turing Test, right? A lot of people have claimed this, but Cameron Jones ( @camrobjones ) and Benjamin Bergen of UCSD actually tested the claim! (Spoiler: The answer is "no, they don't pass.") arxiv.org Does GPT-4 Pass the Turing Test? We evaluated GPT-4 in a public online Turing Test. The best-performing GPT-4 prompt passed in 41% of games, outperforming baselines set by ELIZA (27%) and GPT-3.5 (14%), but falling short of...

Melanie Mitchell, an AI researcher, mentioned an arxiv pre-print “Does GPT-4 Pass the Turing Test?”. Extracting this mention is already useful, and services like Altmetrics collect these mentions as a simple proxy for attention over research. But we can go further with NLP, and try to parse this post into a nanopublication template which contains richer semantics. In this case, there is a template for “Comm enting on or evaluating a paper”. F or demonstration purposes, I played the role of an NLP model and manually parsed the tweet into that template:

This new form with semantics is much more useful than the raw natural language tweet!

Semantics are machine readable, enabling downstream apps to do all kinds of useful operations. For just one example, consider the landing page for papers, like you can find on arXiv or bioRxiv as shown below:

Semantic Nanopublications provide a richer context layer compared with existing social media mention counters

These landing pages often track social media mentions of research, but since those posts lack semantics, all that can be done is a simple count display tracking how many times the research was mentioned. Now imagine we had semantics. We could display how each mention referred to the research in question, providing a much richer context for those who want to explore the discussion about a particular paper (please excuse the crude mockup, this is just for demonstration purposes!).

As mentioned above, this is just one example of many apps enabled by semantic publishing: more on that in future posts!

However, mining social media with NLP is not a cure-all. While it can be extremely useful for bootstrapping the network, it is still only a partial solution: for one, AI tools are still far from perfect, despite truly impressive recent progress. In addition, many platforms have closed off their APIs completely or are making it increasingly costly to extract data. As observed by Barend Mons, one of the leaders of FAIR data and nanopublications, “ Text mining? ...Why bury it first and then mine it again? ”

That’s why we’re also planning to build a nanopublishing social app for researchers to use!

2. Building a decentralized semantic nanopublishing social app

In many cases, using an app for semantic publishing just makes so much more sense than writing unstructured natural language posts on social media, and hoping some AI system in the future will be able to make sense of it. Unlike text mining, semantic publishing can instantly and directly power downstream applications. For example, sharing a review on Goodreads is a form of semantic publishing: the structured data is the review, which the app (Goodreads) can then process in useful ways (computing aggregate ratings for books, showing your friends updates, etc). However, in this example, the published data is also enclosed by Goodreads, meaning that only Goodreads can directly benefit from your data.

By making data open and decentralized, Sensemaking Networks are also designed to be resistant to platform enclosure, therefore enabling a thriving array of diverse applications which leverage participants’ data to benefit the ecosystem. This transformative decoupling of apps and data is certainly not new, and indeed was the dream of early "Web 1.0" Semantic Web pioneers like Tim Berners-Lee. In future posts we can explore the question of what has changed since, given that Web 1.0 didn't work out as anticipated.

What would a semantic nanopublishing app look and feel like?

Basically, just like Twitter, Bluesky, or equivalent apps, but with the key difference that instead of publishing just posts, users can nanopublish, meaning that they can select a template best expressing the semantics of their message.

From posting to semantic nanopublishing

UX/AI assistants will be key in making semantic publishing as easy and natural as posting on existing apps. For example, users can start typing their post in natural language and an AI assistant can suggest suitable templates.

<add Semantic data models>

Cooperative ecosystems, not platforms

Building great infrastructure is a huge task, and a fuller realization of Sensemaking Networks is ultimately beyond the scope of a one person, one year residency. Also, our goal is not to build yet another social media platform! Rather, evolving, interoperable and collaborative ecosystems are both the means and ends of the project. Therefore, we're building partnerships with mission-aligned organizations to create this new ecosystem, together. Shout-outs to some awesome groups we've already started working with (hopefully lots more to come soon!):

DeSci Labs

Knowledge Pixels who are developing developing the nanopublications project

Knowledge Synthesis and Discourse Graphs communities

Shahar Oriel, Daniel Friedman and the rest of the Common SenseMakers

And of course, we're very excited to continue building our collaboration within Astera and its network.

Please reach out at ronen@astera.org if you're interested in joining this effort in any way! We're actively looking for researchers and communities interested in prototyping or development.

That's it for now! 🚀 Please subscribe to this blog if you want to stay updated about project developments, and potential contribution or job opportunities.