ASER: A Large-scale Eventuality Knowledge Graph

By Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, Cyril Leung. 2019

184 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021, methodology): “Most prior art for automatic knowledge graph construction extracts knowledge from raw text (Bhakthavatsalam et al., 2020; Zhang et al., 2020a; Zhou et al., 2020; Zhang et al., 2020b; Li et al., 2020).”
Natural Language Processing with Commonsense Knowledge: A Survey (2021, background): “ASER (activities, states, events, and their relations) [23] is a large-scale eventuality knowledge graph automatically extracted from more than 11-billion-token unstructured textual data.”
TransOMCS: From Linguistic Graphs to Commonsense Knowledge (2020, methodology): “We select [ Wang et al. , 2018 ] and [ Luong et al. , 2015; Zhang et al. , 2020 ] as our base models for the comprehension and the generation task, respectively.”
Guided Generation of Cause and Effect (2020, background): “Event2mind [Rashkin et al. , 2018] 25 K ConceptNet 5.7 [Speer et al. , 2017] 473 K ASER Core [Zhang et al. , 2019] 494 K Atomic [Sap et al. , 2019] 877 K CausalNet [ Luo et al. , 2016 ] 13.3 M Cause Effect Graph (Ours) 89.1 M Post and Vilar [ 2018 ] proposed a variant of lexically-constrained…”
Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023, background): “ATOMIC [72], [73] and ASER [74] focus on the causal effects between events, which can be used for commonsense reasoning.”