Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.

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This paper reports a facile method to fabricate porous carbon nanofibers (PCNFs) via electrospinning polyacrylonitrile/dimethyl sulfone (PAN/DMSO 2 ) pristine fibers followed by preoxidation and carbonization. The nanopores were produced due to the removal of DMSO 2 during the preoxidation process of the nanofibers, without additional chemical or physical activation processes involved. The specific surface and pore density were tunable by varying the PAN/DMSO 2 ratio. The variation in the structure and composition of the nanofibers after heat treatment was characterized by scanning electron microscopy, Fourier transform infrared spectroscopy and energy dispersive X-ray spectroscopy. As a free-standing electrode material in electrochemical capacitors, the PCNFs showed an enhanced electrical double layer capacitance characteristic, confirmed by cyclic voltammetry. The PCNFs were used successfully for capacitive deionization (CDI) with an enhanced desalination amount of 8.1 mg g À1 , 4.5 times higher than that of pure PAN-based CNFs. The good stability demonstrated that these porous carbon nanofibers could be deployed for CDI application.