AI, Blockchain, Open Source - separating the value from the hype

AI, Blockchain and Open Source are terms which continually grab attention, but are they merely buzzwords or will they really disrupt our industry? Ahead of our planned series of webinars on this subject, Jennifer Schivas of 67 Bricks and Nisha Doshi of Cambridge University Press consider how to distinguish hype from reality, and why publishers should care...

AI, Blockchain and Open Source have been generating a lot of attention in the press over the past few years, and high profile announcements from the likes of eLife, Elsevier and Digital Science generate a lot of excitement, but can these technologies really help us improve publishing processes and enhance customer experience?  Can they save us money or help us offer new products and services to authors and researchers?  If so, how do we engage at the right level and the right speed?  How do we ensure the opportunity, if there is one, doesn’t become a threat?

Working at the coal face of publishing innovation means that these are questions we wrestle with on a day-to-day basis, and when we spoke to others at the 2018 ALPSP conference we realised we weren’t alone. Across the industry many of us are exploring options, running pilots, launching products, platforms and systems and putting in place strategies that utilise these new technologies. Some are dipping their toes in the water, while others are diving right in. However, at the other end of the spectrum there are those who dismiss these technologies as mere trends or buzzwords: AI has been around since the 1950s afterall, and isn’t Blockchain regularly described as “just a slow database”?!

So, who is right and who is wrong?  This debate will be at the heart of the forthcoming series of ALPSP webinars, in which we’ll invite industry experts to examine each technology in turn to help us separate the hype from the reality.

In each webinar we will include a short, jargon-free introduction to the technologies and discuss examples of where they are already being used in our industry. We’ll then assess their potential for positive change as well as considering alternative courses of action - which could even include “do nothing” - and look at the recommended first steps publishers can take to begin capitalising on opportunities.

We believe that it is important for publishers to engage with these technologies and make clear decisions with their eyes open. It is not usually wise to invest in cutting edge technology for technology’s sake alone, however there are ways to trial them without undue expense or risk; R&D programmes, pilot projects or collaborative partnerships can all work well.  We will explore how these might be set up to test the waters and release some early benefits before making a major investment or committing to a long-term path.

Join us to start a clear conversation and to begin to separate the hype from the reality. You’ll come away with a better understanding of what these technologies offer in the short, medium and long term, how they might align with wider product, platform or technology strategy, and if and how they might help meet customer needs. There will never be one single answer or one size fits all… so we look forward to some lively conversation!

To find out more about the planned webinars or to book your place please visit https://www.alpsp.org/Webinars/What-is-Hype/62872

Jennifer Schivas Jennifer Schivas is Head of Strategy and Industry Engagement at 67 Bricks, a technology company that helps publishers become more data driven www.67bricks.com

Nisha Doshi

Nisha Doshi is Senior Digital Development Publisher at Cambridge University Press, where she leads the digital publishing team across academic books and journals www.cambridge.org

Artificial Intelligence: What It Is, How It Works, and What Publishers Can Do with It

AI was one of the hot topics at last year's ALPSP conference, in this guest blog Hong Zhou, Senior Product Manager for Information Discovery and AI at Atypon give us the 101 on this transformational development.

Artificial intelligence, or AI, is much more than the latest technology buzzword. According to Gartner, by 2020, AI will positively change the behavior of billions of workers and users. And Tata estimates that the vast majority of those workers will work outside of IT.

But what exactly is AI?

AI is a broad set of technologies that use the computational capabilities of machines to “think” like humans. There are many different types of AI, each of which can be used to solve different problems.

So how can AI be employed by scholarly publishers? Ultimately, any publishing technology should make the research experience more productive, increase content usage, and add value to the publisher’s content. To do that, R&D at Atypon explores ways to help readers discover useful and relevant information more quickly by improving search mechanisms and refining content recommendations.

Making content relevant: Recommender systems

Recommender systems will be familiar to anyone who has received suggestions about what other products to buy before or after making an online purchase. Publishers can use them to target relevant products to individual customers by understanding their online site behavior and interests.

Anticipating what readers want: Personalized search

AI-driven recommendation technology can be extended to personalize search as well: reading histories can be used to adjust search rankings specifically to each user—and even suggest new queries that may be relevant—with the goal of understanding a user’s intentions even before they search.

Faster, easier content classification: Semantic auto-tagging

Content tagging underlies many important website capabilities, such as automating the creation of topic-specific pages and content bundles, and powering search results and content recommendations. But tagging documents and maintaining tag sets can be a daunting undertaking. Auto-taggers powered by intelligent machine learning algorithms tag articles accurately and even identify which tags may not be assigned correctly. They save curators time by letting them concentrate their efforts only on content that’s assigned low “confidence scores” by the auto-tagger, thus making it easier for publishers to implement and manage taxonomies.

Content enrichment: Natural language processing

Keywords are traditionally extracted or selected manually, but doing it automatically requires a large amount of training data to identify relationships among topics and key phrases. By enabling machines to understand the meaning of content rather than just the individual words, they can extract more valuable information from content. Natural language processing (NLP) automates key phrase extraction and obviates “teaching” the engine about the content first. By extracting key phrases from different sections of the content and ranking them based on their importance, NLP ultimately improves content categorization and, by extension, content discovery.

Beyond tagging and metadata: Knowledge graphs

A knowledge graph charts all of the possible connections among publication-related information like authors, topics, journals, articles, and even external knowledge databases. Based on these connections, algorithms identify and recommend to researchers the most influential entities, trending topics, and even co-authors and reviewers based on their areas of specialization and the subjects about which they’re writing.

Granular discoverability for text and images: Semantic enrichment

Suppose a researcher wants to interpret many figures associated with a single experiment. Editors have to segment them manually using specialized software—problematic when processing a large number of them. Machine learning can be used to extract sub-figures and captions from compound figures and even separate labels from their associated images, enabling each item to be searched and retrieved individually. Such automation not only reduces the cost of segmentation but also extracts and organizes more valuable information so researchers can search for, compare, and recommend images more precisely and easily.

Search the science, not the text

AI is no longer an aspirational conversation about the future—many of the technologies discussed above are all available today and in use by publishers. By using AI to provide better search results for researchers—and enable publishers to target content more effectively—publishers can deepen researchers’ engagement with their websites, increase the value of their content, and further the pursuit of scientific knowledge by surfacing the information they need more quickly and accurately.

Hong Zhou works on Atypon’s next-generation information discovery technologies. Previously, he was the CTO of Digital Fineprint, a startup that leveraged machine learning algorithms for the insurance industry. He also spent a year designing race car games at Eutechnyx. He holds a PhD in 3D modeling with artificial intelligence algorithms from Aberystwyth University and has published widely on computer science.

Website: https://www.atypon.com/

Facebook: https://www.facebook.com/Atypon

Linked in: https://www.linkedin.com/company/atypon/

Twitter: https://twitter.com/Atypon

Atypon is the proud sponsor of our Awards Dinner at the ALPSP Annual Conference which will take place on 12-14 September this year.

Spotlight on SourceData - shortlisted for the 2017 ALPSP Awards for Innovation in Publishing

Last but not least in our series of blogs on our 2017 Awards Finalists is EMBO – the creators of SourceData. We speak to Project Leader Thomas Lemberger to find out more:

Tell us a bit about your organisation

EMBO is an international organization that promotes scientific excellence in Life Sciences.It has over 1700 members elected from the leading researchers of Europe and beyond. The organization is funded by 29 member states to provide support to scientists through events, networking opportunities, funding and fellowships for young researchers and shaping science policy. EMBO also publishes four journals reporting important discoveries from the global bioscience community: EMBO Journal, EMBO Reports, Molecular Systems Biology and EMBO Molecular Medicine.

What is the project that you submitted for the Awards?

SourceData is a technology platform made up of several tools that extract information about published figures and make scientific data more discoverable.Through EMBO’s work at the intersection of research and publishing we realized there is a disconnect between the way research data is published in scientific papers and the way researchers typically want to interact with it.  Most scientific papers report the results of carefully-designed experiments producing well-structured data. Unfortunately, during the publishing process this data is typically summarised in text and graphs and “flattened down” thus losing a lot of valuable information along the way.   As a result, it can be very difficult for researchers to find answers to relatively simple questions because data is inaccessible.

For example, it is currently very cumbersome for a scientist to find specific experiments where a certain small molecule drug has been tested on a specific cancer cell line or to look at the results of a published experiment and find out whether similar data had been published elsewhere. These are the kinds of scenario where SourceData can help. SourceData goes to the heart of the scientific paper - the data - and extracts its description in a usable format that researchers can access and interrogate. It then goes on to link this data to results from other scientific papers that have been through the same process.

 

SourceData - Making Scientific Data Discoverable from SourceData on Vimeo.
 

Tell us more about how it works and the team behind it

With SourceData, EMBO has developed a way to represent the structure of experiments. The principle of SourceData is rather simple: we identify the biological objects that are involved in the experiment and then we specify which objects were measured to produce the data and which, if any, were experimentally manipulated by the researchers. Despite its apparent simplicity, this method allows us to build a scientific knowledge graph that turns out to be a very powerful tool for searching and linking papers and their data.

The development of SourceData has been a collaborative process involving the Swiss Institute of Bioinformatics who provided their expertise in developing software platforms in the field of Life Sciences and the curation of data.  After this we worked with Wiley to implement SourceData within a publishing environment. Nature also contributed content to the initiative.

Why do you think it demonstrates publishing innovation?

SourceData transforms the way that researchers can interact with scientific papers by getting to the heart of the paper - the data, and putting it into a highly searchable form. It then takes this a step further by linking this data with relevant results from other scientific papers so that researchers can explore these connections.SourceData can give readers a new level of confidence in finding more of the research that is relevant to their questions. It can give scientists more opportunities to have their publications found and cited and can allow publishers to expose more of their content to interested readers by making it even easier to search and explore.

What are your plans for the future?

Our work to date has involved a lot of manual work so we are now working to automate this process. We are developing artificial intelligence algorithms using deep learning to extract the structure of an experiment from their descriptions in natural language.  Our vision is to provide access to our technology to as many publishers as possible and encourage the widespread adoption of SourceData. In doing so we hope to facilitate access to the data behind more and more journals over time and ultimately accelerate Science in the process.


Thomas Lemberger is leading the SourceData project and is passionate about the importance of scientific data and structured knowledge in publishing. Trained as a molecular biologist, Thomas is Deputy Head of Scientific Publications at EMBO and Chief Editor of the open access journal Molecular Systems Biology.

Twitter: https://twitter.com/embocomm
Facebook: https://www.facebook.com/EMBO.excellence.in.life.sciences

See the ALPSP Awards for Innovation in Publishing Finalists lightning sessions at our Annual Conference on 13-15 September, where the winners will be announced. 

The ALPSP Awards for Innovation in Publishing 2017 are sponsored by MPS Ltd.