|Linked Neuron Data (LND): A Platform for Integrating and Semantically Linking Neuroscience Data and Knowledge|
|Yi Zeng(曾毅); Dongsheng Wang(王东升); Tielin Zhang(张铁林); Bo Xu(徐波); Ceng Y(曾毅)|
|会议录名称||Frontiers in Neuroinformatics|
|会议日期||August 25-27, 2014|
Linked Neuron Data (LND) is an effort and a Web-based platform for integrating and semantically linking Neuroscience data and knowledge from multiple scales and multiple data sources together to support comprehensive understanding of the brain. Currently LND integrates structured neuroscience knowledge from Allen Brain Atlas, NeuroLex, NIF Ontology, NeuroMorpho, Mesh terms, etc. It also extracts declarative domain knowledge from unstructured sources such as PubMed abstracts, Neuroscience literatures and books and the extracted knowledge is represented as triples, such as < apical dendrite, part of, pyramidal neuron> (247,239 triples are extracted by using pattern based information extraction. Considering the quality of the extracted triples, current extractions focus on is-a, part-whole, synonyms relations, and attribute value pairs for specific entities, etc.). All the integrated and extracted knowledge are represented in RDF/OWL. Currently, Linked Neuron Data contains 2,567,178 semantic knowledge triples that describe various declarative knowledge on Neuroscience, including: (1) hierarchical organizations of the brain (with hierarchical brain regions, types of neurons in the specific region, etc. as shown in Figure 1); (2) links among different brain components from different species; (3) relationships among brain components, brain diseases and cognitive functions (including 25,497 triples, extracted from PubMed articles, and Wikipedia pages); (4) facts about brain components (e.g. location distributions, functions, and neurotransmitters of certain types of neurons).
|关键词||Brain Data Ontology|
|作者单位||Institute of Automation, Chinese Academy of Sciences, China|
|Yi Zeng,Dongsheng Wang,Tielin Zhang,et al. Linked Neuron Data (LND): A Platform for Integrating and Semantically Linking Neuroscience Data and Knowledge[C],2014.|