Project Summary The number of biomedical publications is growing at an accelerated speed. This ever-increasing amount of scientific literature has made reading all the published articles regularly impossible even for a very specific research area. The large volumes of scientific publication have also made it very challenging for modern search engines to find relevant articles accurately for a given query. Missing important prior studies in literature search can have serious consequences such as wasting resources/time and/or making wrong scientific conclusions. Another unmet challenge in literature search is that researchers often prefer finding articles where the queries they use are part of the new discoveries, instead of the background knowledge in the articles. The current search engines cannot distinguish between new discoveries and background knowledge in an article. Related to this challenge is that it can be difficult to identify the latest discoveries in a particular scientific area without reading all the recently published articles. To address these challenges, one can convert unstructured text data into structured form, which can then support highly accurate information retrieval, information integration and automated knowledge discovery. A plausible approach for converting unstructured text into structured form is to use named entity recognition (NER) and relation extraction (RE) methods to identify the biological entities and extract their relations to construct knowledge graphs (KGs). KGs can link concepts within existing research to allow researchers to find connections that may have been difficult to discover without them. The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical, publication-free text to create KGs. In addition to entities and relations, the manually annotated dataset provided by LitCoin also contains the annotations of relations being new discoveries or background knowledge. Our team has participated in the challenge and ranked the first place. This application aims to apply the methods we have developed for LitCoin to all PubMed abstracts and PMC full-text articles to build the largest scale KG to date and develop applications on top of it. Specifically, we will (1) develop a knowledge visualization and navigation tool combined with a deep learning-powered search engine we developed previously; (2) develop advanced relation search functions to allow knowledge discovery applications such as drug repurposing and adverse effect discovery; (3) develop functions that allow users to search specifically the new discoveries in articles; and (4) develop functions that return the latest discoveries in a scientific area for a given time period.

Project narrative This application aims to apply an award-winning pipeline to construct the largest-scale knowledge graph (KG) to date using all PubMed abstracts and PubMed Central full-text articles. Applications that are previously infeasible will then be developed on top of the KG including KG-based knowledge visualization, knowledge navigation and knowledge discovery. As our model is able to distinguish between new discoveries and background knowledge in scientific articles, we will also develop novel tools taking advantages of this new capability.