Phase 3 activities will focus on refining and extending xDD and COSMOS APIs that expose data and information in the published scientific literature. Our overarching objective is to develop and deploy an end-to-end platform and AI-powered technical assistant capable of advanced data and information retrieval and question answering over the scientific literature in near real-time, as it is published. We anticipate serving as a key resource in the data and model visualization, assessment and exploration interfaces being developed by ASKE team members.
xDD is currently one of the world’s largest sources of full-text scientific publications and is perhaps unique in it’s ability to support key components of ASKE 3. Our goals are to continue growing the xDD corpus, particularly with publishers and journals relevant to SARS-CoV-2, enhance and scale API-based mechanisms to locate and extract fine-grained information from document full-texts, and develop code container templates for collaborator software deployment over document full-texts.
In addition to releasing public versions of the COSMOS API, which allows for table, figure, and equation retrieval from the full-text of COVID-19 publications, we will build on recent advances in weakly supervised knowledge base construction, pushing the field in new directions to build tools that are useful as part of a broader e↵ort to accelerate scientific discovery. We have been working on learnable contextual retrieval models that can be used for recommending fine-grained segments from new documents and for more accurate and complete question answering from natural language descriptions of target results. The output of this system is a collection of related passages, and experiments are showing that it is more accurate than many keyword-based systems. Our goal is to bring our new system, called Re-Flex, into COSMOS and then address the challenge of scaling it to perform well to live queries operating over large and rapidly growing corpora. Succeeding will allow for more rapid and fully contextualized responses to expert diagnostic and prognostic queries.
An overview of our Phase 1 activities, position relative to TA1 and TA2 ASKE objectives, and initial linking of key elements of model code to elements of our model-created KBC in Phase 2 (and beyond the scope of this project) is provided in our Phase 2 Workshop summary report.
A timeline with links to project milestone deliverables is found on our project Wiki.
Develop data extraction and integration methods to collect data and model metadata (from text). Extracted information will correspond to specified scientific model boundary conditions and parameterizations and will be used to validate and assess model output.
- cGENIE: widely used community Earth systems model, oriented around carbon (https://github.com/UW-COSMOS/cgenie.muffin)
Design and implement methods for 1) automatic extraction of model parameterizations from tables, and 2) extraction of data from tables and figures. Implementation of methods that link data back to model code and the knowledge base developed during Task 1 will enable human-in-the-loop automated refinement ad assessment of scientific models.
A high-level overview of our Phase 2 activities and status is summarized in our DARPA Demo Day Poster. Please see this recorded video of our live demo (the video was recoreded at UW-Madison prior to the September 12, 2019 Demo Day).
A requested follow up webinar and live demo for our DARPA Demo Day presentation was delivered to the NIH IMAG group on 3 Oct. 2019 (PDF of the slides). Video of the webinar is broken into three components: xDD, COSMOS, and a live demo.
An overview of project motivations, our Demo Day components, and our anticipated next steps was delievered on October 18th to ASKE leadership (PDF of presentation).
Our preliminary project general website is cosmos.wisc.edu.
See our Milestone 3 Report for an overview of Phase 1 outcomes and links to relevant repos. A manuscript describing our method for visual object detection with region embeddings is available as a preprint.
Design and implement methods to extract models and semantically meaningful components of models that are reported in the literature in the form of equations. Incorporate weakly supervised KBC systems for such models into the ASKE pipeline.
Develop methods that extend xDD with a scalable table, figure, and equation extraction engine that leverages state-of-the-art methods.
Our end-to-end pipeline for Phase 1 is documented and available as docker image here in our cosmos-demo repo. See linked repos for individual code components.
All development work supported by DAPRA ASKE HR00111990013 and UW-Madison.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this repo except in compliance with the License. You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
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