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1. Mining Biomedical Publications With The LAPPS Grid

   Nancy Ide, Keith Suderman (January 2018, Proceedings of the Eleventh International Conference on Language Resources and Evaluation)

   It is widely recognized that the ability to exploit Natural Language Processing (NLP) text mining strategies has the potential to increase productivity and innovation in the sciences by orders of magnitude, by enabling scientists to pull information from research articles in scientific disciplines such as genomics and biomedicine. The Language Applications (LAPPS) Grid is an infrastructure for rapid development of natural language processing applications (NLP) that provides an ideal platform to support mining scientific literature. Its Galaxy interface and the interoperability among tools together provide an intuitive and easy-to-use platform, and users can experiment with and exploit NLP tools and resources without the need to determine which are suited to a particular task, and without the need for significant computer expertise. The LAPPS Grid has collaborated with the developers of PubAnnotation to integrate the services and resources provided by each in order to greatly enhance the user’s ability to annotate scientific publications and share the results. This poster/demo shows how the LAPPS Grid can facilitate mining scientific publications, including identification and extraction of relevant entities, relations, and events; iterative manual correction and evaluation of automatically-produced annotations, and customization of supporting resources to accommodate specific domains. 

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2. Reverse spatial top-k keyword queries

   https://doi.org/10.1007/s00778-022-00759-9  

   Ahmed, Pritom; Eldawy, Ahmed; Hristidis, Vagelis; Tsotras, Vassilis J. (July 2022, The VLDB Journal)

   Abstract We introduce theReverseSpatial Top-kKeyword (RSK)query, which is defined as:given a query term q, an integer k and a neighborhood size find all the neighborhoods of that size where q is in the top-k most frequent terms among the social posts in those neighborhoods. An obvious approach would be to partition the dataset with a uniform grid structure of a given cell size and identify the cells where this term is in the top-k most frequent keywords. However, this answer would be incomplete since it only checks for neighborhoods that are perfectly aligned with the grid. Furthermore, for every neighborhood (square) that is an answer, we can define infinitely more result neighborhoods by minimally shifting the square without including more posts in it. To address that, we need to identify contiguous regions where any point in the region can be the center of a neighborhood that satisfies the query. We propose an algorithm to efficiently answer an RSK query using an index structure consisting of a uniform grid augmented by materialized lists of term frequencies. We apply various optimizations that drastically improve query latency against baseline approaches. We also provide a theoretical model to choose the optimal cell size for the index to minimize query latency. We further examine a restricted version of the problem (RSKR) that limits the scope of the answer and propose efficientapproximatealgorithms. Finally, we examine how parallelism can improve performance by balancing the workload using a smartload slicingtechnique. Extensive experimental performance evaluation of the proposed methods using real Twitter datasets and crime report datasets, shows the efficiency of our optimizations and the accuracy of the proposed theoretical model. 

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3. Griddle: Effective Query Support over Voluminous Gridded Spatial Datasets

   https://doi.org/10.1109/BigData55660.2022.10020796  

   Smith, Pierce; Pallickara, Sangmi Lee; Pallickara, Shrideep (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   Gridded datasets occur in several domains. These datasets comprise (un)structured grid points, where each grid point is characterized by XY(Z) coordinates in a spatial referencing system. The data available at individual grid points are high-dimensional encapsulating multiple variables of interest. This study has two thrusts. The first targets supporting effective management of voluminous gridded datasets while reconciling challenges relating to colocation and dispersion. The second thrust is to support sliding (temporal) window queries over the gridded dataset. Such queries involve sliding a temporal window over the data to identify spatial locations and chronological time points where the specified predicate evaluates to true. Our methodology includes support for a space-efficient data structure for organizing information within the data, query decomposition based on dyadic intervals, support for temporal anchoring, query transformations, and effective evaluation of query predicates. Our empirical benchmarks are conducted on representative voluminous high dimensional datasets such as gridMET (historical meteorological data) and MACA (future climate datasets based on the RCP 8.5 greenhouse gas trajectory). In our benchmarks, our system can handle throughputs of over 3000 multi-predicate sliding window queries per second. 

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4. COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

   https://doi.org/10.18653/v1/2021.emnlp-main.305  

   Zhang, Xinliang; Sun, Heming; Yue, Xiang; Lin, Simon; Sun, Huan (November 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)

   We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at https://github.com/sunlab-osu/covid-faq. 

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5. The LAPPS Grid/Galaxy Platform for Mining Scientific Publications

   Nancy Ide​, Keith Suderman (July 2019, Galaxy Community Conference 2019)

   It is widely recognized that the ability to exploit Natural Language Processing (NLP) text mining strategies has the potential to increase productivity and innovation in the sciences by orders of magnitude, by enabling scientists to pull information from research articles in scientific disciplines such as genomics and biomedicine. These methods enable scientists to rapidly identify publications relevant to their own research as well as make scientific discoveries by scouring hundreds of research papers for associations and connections (such as between drugs and side effects, or genes and disease pathways) that humans reading each paper individually might not notice. The goal of our work is to enable rapid development of workflows for mining scientific publications and, crucially, means to adapt tools and workflows to data for specific disciplines (domain adaptation). Our work on this project is still ongoing; however, as a starting point we initially addressed the needs for mining biomedical publications, and a Galaxy instance of the LAPPS Grid tailored to mining biomedical publications is currently maintained on the JetStream cloud environment (https://jetstream.lappsgrid.org). 

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