The Artificial Intelligence industry regularly develops applications that
mostly rely on Knowledge Bases, a data repository about specific, or general,
domains, usually represented in a graph shape. Similar to other databases, they
face two main challenges: information ingestion and information retrieval. We
approach these challenges by jointly learning graph extraction from text and
text generation from graphs. The proposed solution, a T5 architecture, is
trained in a multi-task semi-supervised environment, with our collected
non-parallel data, following a cycle training regime. Experiments on WebNLG
dataset show that our approach surpasses unsupervised state-of-the-art results
in text-to-graph and graph-to-text. More relevantly, our framework is more
consistent across seen and unseen domains than supervised models. The resulting
model can be easily trained in any new domain with non-parallel data, by simply
adding text and graphs about it, in our cycle framework.

该研究提出了一种基于 T5 架构的多任务半监督环境下联合学习文本图谱提取和图谱生成的解决方案，此方法在 WebNLG 数据集中表现优于无监督最新成果，且相较于有监督模型更具一致性。

一个多任务半监督框架用于 Text2Graph 和 Graph2Text

A multi-task semi-supervised framework for Text2Graph & Graph2Text

Biological neural networks define the brain function and intelligence of
humans and other mammals, and form ultra-large, spatial, structured graphs.
Their neuronal organization is closely interconnected with the spatial
organization of the brain's microvasculature, which supplies oxygen to the
neurons and builds a complementary spatial graph. This vasculature (or the
vessel structure) plays an important role in neuroscience; for example, the
organization of (and changes to) vessel structure can represent early signs of
various pathologies, e.g. Alzheimer's disease or stroke. Recently, advances in
tissue clearing have enabled whole brain imaging and segmentation of the
entirety of the mouse brain's vasculature. Building on these advances in
imaging, we are presenting an extendable dataset of whole-brain vessel graphs
based on specific imaging protocols. Specifically, we extract vascular graphs
using a refined graph extraction scheme leveraging the volume rendering engine
Voreen and provide them in an accessible and adaptable form through the OGB and
PyTorch Geometric dataloaders. Moreover, we benchmark numerous state-of-the-art
graph learning algorithms on the biologically relevant tasks of vessel
prediction and vessel classification using the introduced vessel graph dataset.
Our work paves a path towards advancing graph learning research into the field
of neuroscience. Complementarily, the presented dataset raises challenging
graph learning research questions for the machine learning community, in terms
of incorporating biological priors into learning algorithms, or in scaling
these algorithms to handle sparse,spatial graphs with millions of nodes and
edges. All datasets and code are available for download at
this https URL .

本文利用组织清除技术提出了基于特定成像方案的整个小鼠脑血管图数据集，并在生物学相关任务中使用最先进的图学习算法进行了基准测试，从而为神经科学领域的图学习研究开辟了道路。