Abstract
Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.
Highlights
Managing neuroscience literature from species perspective is an innovative and important research task for understanding the functionality and structure of the brain
Species information in scientific works can be used to organize knowledge facts in the Linked Brain Data1 (LBD) (Zeng et al, 2014b) scheme, and the system composed of brain and neuroscience communities (Ascoli et al, 2007; Gardner et al, 2008; Imam et al, 2012; Sunkin et al, 2012; Larson and Martone, 2013; Poo et al, 2016), neurorobotics, and other devices can automatically utilize species knowledge on the Internet by accessing the API provided by the LBD platform
A first observation is that hierarchical models (H-LSTM and HCNN) achieve similar results with the corresponding singlelevel models (LSTM and CNN) on the PubMed dataset
Summary
Managing neuroscience literature from species perspective is an innovative and important research task for understanding the functionality and structure of the brain. Species information in scientific works can be used to organize knowledge facts in the Linked Brain Data (LBD) (Zeng et al, 2014b) scheme, and the system composed of brain and neuroscience communities (Ascoli et al, 2007; Gardner et al, 2008; Imam et al, 2012; Sunkin et al, 2012; Larson and Martone, 2013; Poo et al, 2016), neurorobotics, and other devices can automatically utilize species knowledge on the Internet by accessing the API provided by the LBD platform. The species classification task is to assign pre-defined species labels to neuroscience literature that does not explicitly mention the species. This technology can be used to classify and organize neuroscience literature based on the species to help researchers and devices compare the similarities and differences between different species for linking the brain and neuroscience communities and different devices.
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