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Salem, N. M.

Publications and source records attributed to Salem, N. M..

2 recordsLinked to original sources

Creating an Ignorance-Base: Exploring Known Unknowns in the Scientific Literature

BackgroundScientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. ResultsWe present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development), which were buried among the many standard enriched concepts, by searching for concepts enriched in ignorance statements. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. ConclusionOur goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/519634v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1edb6e6org.highwire.dtl.DTLVardef@182bf3corg.highwire.dtl.DTLVardef@d90312org.highwire.dtl.DTLVardef@1589433_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe created the first ignorance-base (knowledge-base) to capture goals for scientific knowledge C_LIO_LIOur exploration methods provide analyses, summaries, and visualizations based on a query C_LIO_LIIgnorance enrichment provided fruitful avenues for future research C_LIO_LIExploration by topic in vitamin D found three avenues to explore C_LIO_LIExploration by experimental results for vitamin D and preterm birth found an emerging topic C_LI

bioinformatics↗

Identifying and Classifying Goals For Scientific Knowledge

MotivationScience progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize ignorance statements, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. ResultsWe present a novel ignorance taxonomy driven by the role ignorance statements play in the research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10,000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AvailabilityCorpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python. ContactMayla.Boguslav@CUAnshcutz.edu

bioinformatics↗