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Welcome to the New Era of Scientific Publishing

— by Tobias Kuhn, 4 April 2022; reporting on work with Cristina Bucur, Davide Ceolin, and Jacco van Ossenbruggen [1,2] —

I believe we have made the first steps venturing into a new era of scientific publishing. Let me explain. [Update: At what point exactly a new era begins and what counts as first steps are of course subject to debate. I therefore added a section on related initiatives and further reading below.]

Science is nowadays communicated in a digital manner through the internet. We essentially have a kind of "scientific knowledge cloud", where researchers with the help of publishers upload their latest findings in the form of scientific articles, and where everybody who is interested can access and retrieve these findings. (This is in fact only true for articles that are published as Open Access, but that is not the point here.)

In a sense, this scientific knowledge cloud has been a big success, but it also has serious limitations. Imagine somebody wanting to build an application that feeds on all these findings, for example to help researchers learn about interesting new developments or to summarize scientific consensus for laypeople. Concretely, a developer might face a task like this one:

Retrieve all genes that have been found to play a role in a part of the respiratory system in Covid-19 patients. Only include results from randomized controlled trials published in the last seven days.

To a naive developer without experience in how scientific knowledge is communicated, this might sound quite easy. One would just have to find the right API, translate the task description into a query, and possibly do some post-processing and filtering. But everybody who knows a bit how science is communicated immediately realizes that this will take a much bigger effort.

Why Text Mining is not the Solution

The problem is that scientific results are represented and published in plain text, and not in a structured format that software could understand. So, in order to access the scientific findings and their contexts, one has to apply text mining first. Unfortunately, despite all the impressive progress with Deep Learning and related techniques in the past few years, text mining is not good enough, and probably will never be.

To illustrate the point, we can look at the results of the seventh BioCreative workshop held in November 2021, where world-leading research teams competed in extracting entities and relations from scientific texts. Just to detect the type of a relation between a given drug and a given gene out of 13 given relation types, the best system achieved an F-score of 0.7973.

But that is just the relation type. To get the full relation out, we first have to know the entity on the left-hand side (subject) and right-hand side (object) of the relation. We can look at a different task of the BioCreative workshop to get a feeling of how well extracting these subjects and objects work. The task focussed on extracting chemicals, and this is done it a two-stage process. First the entities are recognized in the text, with an F-score of the best system of 0.8672, and then the recognized chemicals are linked to the corresponding formal identifiers, with the best F-score being 0.8136:

A very rough back-of-the-envelope calculation can give us an estimate of the quality of mining such entire relations:

An overall F-score of 0.40, as resulting from this calculation, roughly means that around 60% of retrieved relations are wrong and 60% of existing relations are not retrieved. This is clearly not even close to good enough for most types of possible applications. And mind you, these numbers come from the best performing systems when world-leading research groups compete and probably put months of effort into optimizing their systems for the specific problem. Moreover, we are talking here only about the simplest possible kind of relations of the form subject-relation-object.

This seems to point to a deeper problem. Text mining is just a work-around, and the real problem is elsewhere. As Barend Mons rhetorically asked "Why bury it first and then mine it again?". Instead of seeing text mining as the ultimate solution, we should just stop burying our knowledge in the first place.

In the work I will explain below, we wanted to find out how we can practically publish findings without burying them.

Representing Scientific Findings in Logic

As a first step to experiment with such a new way of publishing, we needed to find a general way of how to represent high-level scientific findings in some sort of formal logic. Even though such findings are arguably the most important outcome of science, there was no prior work on practically mapping (most of) these findings to formal logic across domains. To better understand the logical structure of such high-level scientific findings (e.g. that a gene tends to have a certain effect on the course of a given disease), we selected a random sample of 75 research articles from Semantic Scholar.

Studying the high-level findings from these random articles, we managed to elicit a wide-spread logical pattern, which we then turned in the super-pattern model. It consists of five slots to be filled, and translates directly to a logical formula. Our paper [1] explains the details, and I give here just one example. The finding of the article entitled "P‐glycoprotein expression in rat brain endothelial cells: evidence for regulation by transient oxidative stress" can be expressed with the super-pattern as follows:

  • Context class: rat brain endothelial cell
  • Subject class: transient oxidative stress
  • Qualifier: generally
  • Relation: affects
  • Object class: Pgp expression

The three class slots take any class identifier (here shown by class name for simplicity), whereas the qualifier and the relation come from closed lists of a few possible values. Informally, the example above means that whenever there is an instance of "transient oxidative stress" in the context of an instance of "rat brain endothelial cell" then generally (being defined as in at least 90% of the cases) it has a relation of type "affects" to an instance of "Pgp expression" that is in the same context. Formally, it corresponds to this logical formula (in a bit of a non-standard notation using conditional probability):

Once we discovered this pattern we tried to formalize the 75 high-level findings, and this was the result:

68 of the 75 findings, shown in blue, could be represented with the super-pattern. And the remaining 7 findings, shown in red, are in fact easier to represent, as they can be captured with a simple subject-relation-object structure. (So these latter simpler structures are the ones that existing text mining approaches struggle with, and it is safe to assume that they would perform even worse on the more complicated statements here shown in blue.)

So, it seems that we have found a logical pattern that allows us to represent most high-level scientific findings from different disciplines.

Stepping into the new Era of Scientific Publishing

Next, we wanted to make a serious practical step into the new era where findings are machine-interpretable from the start. We designed this as a kind of field study with a special issue of machine-interpretable papers at an existing journal. We wanted these papers to look like regular papers to those who want to look at them in that way, but they should also come with representations in formal logic for anyone or anything that knows how to deal with that. For that special issue, we chose the journal Data Science, of which I am an editor-in-chief.

We also had to make a practical concession though: While the whole setup could be used to publish novel findings, we restricted ourselves to findings from existing publications. For that, we introduced the concept of a formalization paper whose novel contribution is the formalization of an existing finding. So, authors of a formalization paper take credit for the formalization of the finding, but not for the finding itself.

To represent these findings and thereby the formalization papers, we used the nanopublication format and the Nanobench tool. Researchers who contributed to this special issue filled in a form to express and submit their formalization that looked like this:

Apart from the actual formalization in the assertion part of the nanopublication (blue), this also involved specifying the provenance of that formalization (red) and further metadata (yellow). The provenance in this case states that the assertion was derived by a formalization activity taking an existing paper as input. This interface provides auto-completing drop-down menus and maps the result to the logic language RDF in the back. I won't go into the details of defining new classes and reviewing (both done with nanopublications and Nanobench too), but you can have a look at the preprint of our paper [2] on that.

We ended up with 15 formalization papers in our special issue, as summarized by this table:

AuthorsCONTEXTSUBJECTQUALIFIERRELATIONOBJECT
1Joslinearly human adipogenesisregulatory element within the first intron of FTOgenerallyaffectsexpression of genes IRX3 and IRX5
2Nicholshuman motor neuronTAR DNA binding proteincan generallycontributes totranscription of stmn2
3Mietchendejellied fertilizable stage VI Xenopus laevis oocytestrong static magnetic fieldgenerallyaffectscell cortex
4Ehrhart, Evelo(no context class)genes associated with CAKUTsometimesis same astargets of vitamin A
5Patrinospatient undergoing PCIpharmacogenomics guided clopidogrel therapygenerallyenablescost-effective treatment
6Martoranahumansmoothened signaling pathwaymostlyaffectsastrocyte development
7Mietchen, Penev, Dimitrovabiodiversity datalicense with non-commercial clausegenerallyinhibitsdata reuse
8Dimitrovarelease of OpenBiodiv knowledge graphtriple in OpenBiodiv knowledge graphgenerallyis same assemantic triple extracted from biodiversity literature
9BrauerUNC13ATAR DNA binding proteingenerallyinhibitsinclusion of cryptic exon
10Dumontierdata setadherence to the FAIR guiding principlescan generallyenablesautomated discovery
11Queralt RosinachhumanNGLY1 deficiencyalwaysis caused bydysfunction of ERAD pathway
12Usbecksocial grouprelative neocortex sizeneveraffectssocial group size
13Bainerecm bound cancer cellglycocayx bulkgenerallyincreasesintegrin clustering
14Grouès, Vega Moreno, SatagopamhumanSTX1B mutationfrequentlyco-occurs withepilepsy
15de Boerdigital humanities researchusage of Linked Data Scopescan generallycontributes totransparency

Each row of this table corresponds to a formalization, and can be written out as a logic formula. So, these papers look here quite different from what we are used to. But if you go to the official page for the special issue on the publisher's website, they look like regular papers. Besides the regular button to download the paper as a PDF, there is also a link that points to the nanopublication representation, thereby connecting the two ways of looking at this paper:

So, for the first time, software can reliably interpret the main high-level findings of scientific publications. This special issue is a just a small first step, but it could prove to be the first step into a new era of scientific publishing. The practical immediate consequences of this might seem limited, but I think the longer-term potential of making scientific knowledge accessible to the interpretation by machines is hard to overstate.

References

  1. Cristina-Iulia Bucur, Tobias Kuhn, Davide Ceolin, Jacco van Ossenbruggen. Expressing high-level scientific claims with formal semantics. In Proceedings of K-CAP '21. ACM, 2021.
  2. Cristina-Iulia Bucur, Tobias Kuhn, Davide Ceolin, Jacco van Ossenbruggen. Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers. arXiv 2203.01608, 2022.

For completeness, I list here some related initiatives and further reading. I only include references where machine-interpretable representations of findings are part of or treated as scientific publications.

You can write a comment below to suggest additions to this list.

License

This text is available under the CC BY 4.0 license. The images were created with Excalidraw, using several of its libraries of visual elements, and are available under the MIT license.

@jhpoelen
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jhpoelen commented Apr 4, 2022

@tkuhn thanks for sharing your notes on your progress in contributing to semantic publishing of scientific knowledge. And, as I am sure you know, many of your colleagues also made "[...] steps venturing into a new era of scientific publishing [...]" . For instance, from my own experience, I know what https://plazi.org (Plazi), Pensoft Publishing https://pensoft.net, and Zenodo https://zenodo.org have been active in semantic publishing.

You can find some of my experiences with them at:

https://globalbioticinteractions.org/plazi-zenodo @myrmoteras @slint

https://globalbioticinteractions.org/pensoft @mdmtrv

https://globalbioticinteractions.org/covid19 @n8upham @qgroom @kephelps @arw36

So, I was wondering whether it'd be beneficial to mention these, or similar, other activities to highlight that the ideas have been circulating for a while, and significant investments have been made to help work towards a way of scientific knowledge that allows for easier integration, aggregation, and analysis on a large scale.

@n8upham
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n8upham commented Apr 4, 2022

Very nice to hear about this work @tkuhn ! Yes, as @jhpoelen mentioned, our Biodiversity Knowledge Hub taskforce has been thinking a lot about the creation of semantic knowledge graphs for ecological interaction data -- specifically as relates to mammals, viruses, and our evidence of their interactions (including type of assay used, etc). We summarized some of these points last year in this Lancet Planetary Health article, including as relates to the extraction of old data (Plazi), new ways of publishing (Pensoft), and how to connect all these data (GloBI): https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00196-0/fulltext

It would be great to connect our group with yours to think about this more.
Cheers, --nate

@myrmoteras
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myrmoteras commented Apr 5, 2022

Since I am mentioned: there is an effort into what you call the new age in publishing that we "declared" in 2010 with this http://doi.org/10.3897/zookeys.50.543 based on the Linnaeus 250 year symposium in Paris where we started talking about data in publication and semantics () which then let to a formalization in collaboration with the NIH and Pensoft (https://doi.org/10.5281/zenodo.3484285) and an implementation in Zookeys, as the first XML semantic enhanced publication (https://doi.org/10.5281/zenodo.804247). More on the history of all of this is here,

Probably the three most relevant annotations are taxonomic treatment, treatment citation, and material citation. They essentially allow to link taxonomic treatment and resonstruct synonym, allow not just citation bat access to data, and in material citation to link between a specimen cited in the literature to the actual physical specimen, and from the physical specimen to what is known about it in the literature. This is mostly the system in biologist head and they can follow by knowing what those citations mean. A fledgling system is TreatmentBank where the publications are processed, annotated and made accessible for further re-use. There are close to 40K articles as part of GBIFs record, or they can be searched through Synospecies, a tool that is build on treatment citations.

An effort is also underway to extract tables and make them reusable, for example to extract data taxononic name, accession code, specimen code, distribution.

@tkuhn
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tkuhn commented Apr 5, 2022

@jhpoelen and @n8upham, thank you for your comments. Indeed, there has been a lot of existing work in this direction, and I didn't want this post to give a wrong impression on this. I just added an update and a section with related initiatives and further reading. I added your Viewpoint paper to that list.

@jhpoelen, I am of course aware of your general work with Pensoft, but probably not up-to-date with the details and the latest developments. I have added GloBI and some of the integrations you mentioned in the list. Do you maybe have a more precise link or reference where you explain how this connects to the publications? I would like to keep this list of related work focussed, to only include approaches where high-level findings are machine-interpretable and a proper part of (or treated as) scientific publications. If you have a reference that is more specific on that, let me know, so I could update the entry above.

@myrmoteras
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myrmoteras commented Apr 5, 2022

@tkuhn here is an example of the annotation of biotic interaction in literature and represented as custom metadata using in this case biotic interactions terms.
https://zenodo.org/record/3949282#.Ykv4OehBz8A or see https://zenodo.org/record/3949282/export/json#.Ykv5RehBz8A

this is part of the Coronavirus-Host Community launched by the CETAF COVID task forece at Zenodo, and which has been harvested by GLoBI.

@tkuhn
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tkuhn commented Apr 5, 2022

@myrmoteras, thank you for your comments and providing these explanations and references. These works seem indeed highly relevant. I have just a few questions for my understanding:

  • To what extent are the authors involved in the formal representations of these taxonomy descriptions? From at least one of the references you provided they seem to come from the publisher, not the authors, and are added only once the paper has been accepted.
  • Do these formal representations also cover the main findings of a paper?
  • In the JSON example you link above, are the subjects and objects like "Chaerephon plicata" also linked to unique identifiers?

These things are probably answered in the documents you provided somewhere, but I will need some more time to read through them.

@myrmoteras
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myrmoteras commented Apr 5, 2022

@tkuhn

author involvement:

  • Direct if publishing in Biodiversity Data Journal using forms; marking up existing publication as part of a research project, such as Jeremy Miller T. rex project with the goal to digitize all the published facts and link them to the respective physical specimens.
  • planned via nanopups in development by Pensoft et al.
  • via the choice of journals for publishing which make the annotations (Pensoft journals, European Journal of Taxonomy, Museum national d'Histoire Naturel journals
  • none but annotation are done via Research project and fundation support, eg Arcadia

main findings

  • the main finding of taxonomic papers are taxonomic treatments, figures, treatment citations and the material citation, upon which the findings are based

taxonomic names

  • taxonomic names are a problem because the community has not yet come to a conclusion how such names can be cited. What we do on our side is to assign for each name usage the related treatment with a DataCite DOI via the Biodiversity Literature Repository , TreatmentBank where we use peristent httpURI, which then are used in Synospecies. In BiCIKL in the forthcoming meeting this IS a central theme. Also in a forthcoming paper on linking - which might be of interest to you.
    if you look at the RDF we use in Synospecies, you see the use of taxonomic name identifiers with example RDF here for questions ping @retog

@tkuhn
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tkuhn commented Apr 5, 2022

@myrmoteras, thanks for these answers and additional pointer. Very interesting! I added some of these to the list of related initiatives above.

The main difference between our initiatives seems to be that you are doing it for a specific field and community (and with specific kinds of findings), whereas we wanted to find out how to it for general high-level scientific findings across disciplines.

@myrmoteras
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myrmoteras commented Apr 5, 2022

@tkuhn I understand - we have a similar look at converting PDFs into "FAIR" data: general aspects are that journals have figures, figure captions and figure citation; bibliographic reference, bibliographic reference citations; tables, table caption, table citations; named entities such as person names, taxonomic names;

Specific are what I described above treatment und elements therein that are biodiversity, even taxonomy specific.

intersting to find out how the general elements can be made specific, such as "interacts" in a historical paper "president Y speaks with president X" and in biological papers "protein Y interacts" with protein Z", and "bird eats worm" or the "worm is the food of bird X". one the one hand interaction, but then it needs more to understand what interacts means, sort of an attribute, which then has to be domain specific.

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