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.