AutoNet: Automated Network Construction and Exploration System from Domain-Specific Corpora

As a collaborative project funded by US Army Research Lab, our goal is to turn massive unstructured text data into structured heterogeneous information networks (HINs), on which actionable knowledge can be further uncovered flexibly and effectively based on user’s instructions. Taking advantage of open knowledge bases, we develop an end-to-end, data-driven system, AutoNet, with no additional human curation and annotation. AutoNet constructs a large-scale HIN from massive (user-provided) domain-specific text corpora (eg, scientific papers) using our innovative phrase mining, entity typing, and relation extraction methods, and saves these models for later usage. After that, AutoNet supports two real-time functions:(1) discovery: given a few user-provided documents, AutoNet will construct a new HIN on the fly and highlight those new nodes (ie, entities) and/or edges (ie, relations), which are not in the pre-stored network; and (2) exploration: given some user-provided keywords, AutoNet will retrieve a related subnetwork from the large pre-stored HIN. We further design effective visualization tools for both functions. A demo video is available 1.