About this Research Topic
Literature-Based Discovery (LBD) aims at generating novel and actionable knowledge from vast, diverse, and seemingly disconnected fragments of information. As a maturing research field, a plethora of methods/algorithms and software systems have previously been published to solve fundamental research problems in LBD.
Notwithstanding the field's current progress, a number of research challenges in LBD remain under-served and constitute emerging areas for the field. For instance, the rapid proliferation of scientific knowledge in their various textual and non-textual forms requires a new generation of LBD methods and algorithms capable of scaling well against terabytes or exabytes of data. Such futuristic LBD systems should also be able to process the incoming streams of scientific information in a real-time manner, enabling early detection of new hypotheses. Not only that, in the current age of pervasive misinformation more intelligent LBD systems are needed to automatically prioritize the most reliable information that leads to high-quality knowledge discovery outcomes. These and many other emerging research topics constitute new frontiers for the field.
For LBD2020 Authors
To foster innovative collaboration and identify grand research challenges about the topic, the editors of this Research Topic have recently organized The First International Workshop on Literature-Based Discovery (LBD 2020) in conjunction with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020) in Singapore. While we strongly encourage manuscript submissions from all researchers, authors who have presented their work in LBD 2020 may submit their substantially extended work to this Research Topic. The editors commit to ensuring a faster, though rigorous, peer-review where possible. Please note that manuscript acceptance is fully subject to a high-quality peer-review process in line with Frontiers journal standard and therefore not guaranteed.
Themes may include, but are not limited to, the following:
• highly scalable LBD methods/algorithms/systems capable of processing a very large amount of scientific data
• hypothesis generation from heterogeneous information sources (e.g. textual and non-textual data, genomic/gene expression/epigenetic/drug binding/protein-protein interaction databases, figures and tables of scholarly publications, videos, Twitter feeds, etc.)
• multi-modal LBD approaches
• real-time LBD, surveillance, and early detection of novel discoveries from literature
• novel applications of LBD to non-biomedical domain
• veracity of hypotheses generated by LBD systems
• new evaluation methods and benchmarking
• usability issues and real user-evaluation studies
• novel visualization techniques
• explainable LBD systems
• investigation of new LBD paradigms, such as discovery by analogy, weak cumulative tests, or novel methods.
Notwithstanding the field's current progress, a number of research challenges in LBD remain under-served and constitute emerging areas for the field. For instance, the rapid proliferation of scientific knowledge in their various textual and non-textual forms requires a new generation of LBD methods and algorithms capable of scaling well against terabytes or exabytes of data. Such futuristic LBD systems should also be able to process the incoming streams of scientific information in a real-time manner, enabling early detection of new hypotheses. Not only that, in the current age of pervasive misinformation more intelligent LBD systems are needed to automatically prioritize the most reliable information that leads to high-quality knowledge discovery outcomes. These and many other emerging research topics constitute new frontiers for the field.
For LBD2020 Authors
To foster innovative collaboration and identify grand research challenges about the topic, the editors of this Research Topic have recently organized The First International Workshop on Literature-Based Discovery (LBD 2020) in conjunction with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020) in Singapore. While we strongly encourage manuscript submissions from all researchers, authors who have presented their work in LBD 2020 may submit their substantially extended work to this Research Topic. The editors commit to ensuring a faster, though rigorous, peer-review where possible. Please note that manuscript acceptance is fully subject to a high-quality peer-review process in line with Frontiers journal standard and therefore not guaranteed.
Themes may include, but are not limited to, the following:
• highly scalable LBD methods/algorithms/systems capable of processing a very large amount of scientific data
• hypothesis generation from heterogeneous information sources (e.g. textual and non-textual data, genomic/gene expression/epigenetic/drug binding/protein-protein interaction databases, figures and tables of scholarly publications, videos, Twitter feeds, etc.)
• multi-modal LBD approaches
• real-time LBD, surveillance, and early detection of novel discoveries from literature
• novel applications of LBD to non-biomedical domain
• veracity of hypotheses generated by LBD systems
• new evaluation methods and benchmarking
• usability issues and real user-evaluation studies
• novel visualization techniques
• explainable LBD systems
• investigation of new LBD paradigms, such as discovery by analogy, weak cumulative tests, or novel methods.
Keywords: literature-based discovery, hypothesis generation, literature mining, text mining, emerging areas
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
