A web application to extract key information from journal articles

“Non-expert readers are thus typically unable to understand scientific articles, unless they are curated and made more accessible by third parties who understand the concepts and ideas contained within them. With this in mind, a team of researchers at the Texas Advanced Computing Center in the University of Texas at Austin (TACC), Oregon State University (OSU) and the American Society of Plant Biologists (ASPB) have set out to develop a tool that can automatically extract important phrases and terminology from research papers in order to provide useful definitions and enhance their readability….”

Paper Digest

Paper Digest uses an AI to generate an automatic summary of a given research paper. You can simply provide a DOI (digital object identifier), or the url to a PDF file, then Paper Digest will return a bulleted summary of the paper. This works only for open access full-text articles that allow derivative generation (i.e. CC-BY equivalent). In case you receive an error message and no summary is generated, it is most likely either the full text is not available to use or the license does not allow derivative generation….”

Underlay

“The Underlay is a global, distributed graph database of public knowledge. Initial hosts will include universities and individuals, such that no single group controls the content. This is an attempt to replicate the richness of private knowledge graphs in a public, decentralized manner….

Powerful collections of machine-readable knowledge are growing in importance each year, but most are privately owned (e.g., Google’s Knowledge Graph, Wolfram Alpha, Scopus). The Underlay aims to secure such a collection as a public resource. It also gives chains of provenance a central place in its data model, to help tease out bias or error that can appear at different layers of assumption, synthesis, and evaluation….

The Underlay team is developing the protocols, first instances, and governing rules of this knowledge graph. Information will be added at first by building focused, interpretive overlays — knowledge curated for a particular audience. Overlays could for instance be journals, maps, or timelines, incorporating many sources of more granular information into a single lens….

[Coming in Phase 2:] A network of Underlay nodes at different institutions, demonstrating local vs global updating. An initial pipeline for extracting structured knowledge and sources from documents to populate lower layers. Tools to sync with existing structured repositories such as Wikidata, Freebase, and SHARE. And tools to visualize what is in the Underlay and how it is being used….”

Taylor & Francis is bringing AI to academic publishing – but it isn’t easy | The Bookseller

“Leading academic publisher Taylor & Francis is developing natural language processing technology to help machines understand its books and journals, with the aim to enrich customers’ online experiences and create new tools to make the company more efficient.

The first step extracts topics and concepts from text in any scholarly subject domain, and shows recommendations of additional content to online users based on what they are already reading, allowing them to discover new research more easily. Further steps will lead to semantic content enrichment for more improvements in areas such as relatedness, better searches, and finding peer-reviewers and specialists on particular subjects….”

Taylor & Francis is bringing AI to academic publishing – but it isn’t easy | The Bookseller

“Leading academic publisher Taylor & Francis is developing natural language processing technology to help machines understand its books and journals, with the aim to enrich customers’ online experiences and create new tools to make the company more efficient.

The first step extracts topics and concepts from text in any scholarly subject domain, and shows recommendations of additional content to online users based on what they are already reading, allowing them to discover new research more easily. Further steps will lead to semantic content enrichment for more improvements in areas such as relatedness, better searches, and finding peer-reviewers and specialists on particular subjects….”

Extracting research evidence from publications | EMBL-EBI Train online

“Extracting research evidence from publications Bioinformaticians are routinely handling big data, including DNA, RNA, and protein sequence information. It’s time to treat biomedical literature as a dataset and extract valuable facts hidden in the millions of scientific papers. This webinar demonstrates how to access text-mined literature evidence using Europe PMC Annotations API. We highlight several use cases, including linking diseases with potential treatment targets, or identifying which protein structures are cited along with a gene mutation.

This webinar took place on 5 March 2018 and is for wet-lab researchers and bioinformaticians who want to access scientific literature and data programmatically. Some prior knowledge of programmatic access and common programming languages is recommended.

The webinar covers: Available data (annotation types and sources) (1:50) API operations and parameters and web service outputs (8:08) Use case examples (16:56) How to get help (24:16)

You can download the slides from this webinar here. You can learn more about Europe PMC in our Europe PMC: Quick tour and our previous webinar Europe PMC, programmatically.

For documentation, help and support visit the Europe PMC help pages or download the developer friendly web service guide. For web service related question you can get in touch via the Google group or contact the helpdesk [at] europepmc.org”>help desk.”

Knowtro

“Knowtro has:

  • Identified elements of knowledge shared across research disciplines and mapped the elements critical to the successful transfer of knowledge from document to user.
  • Built a technology-facilitated process whereby complex analyses can be distilled for ease of discovery and use. Findings from published research papers in only the top academic journals are added to the platform daily.
  • Implemented a search results display feature that (1) uses consistent, logical expressions about research findings rather than happenstance excerpts of text, and (2) prioritizes results not by popularity, but according to validity and usefulness (e.g., research design). …”

Release ‘open’ data from their PDF prisons using tabulizer | R-bloggers

“As a political scientist who regularly encounters so-called “open data” in PDFs, this problem is particularly irritating. PDFs may have “portable” in their name, making them display consistently on various platforms, but that portability means any information contained in a PDF is irritatingly difficult to extract computationally.”