KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion

• URL: http://arxiv.org/abs/2004.13631v2
• Date: Fri, 4 Jun 2021 03:23:10 GMT
• Title: KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
• Authors: Jie Zhou, Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun
• Abstract summary: A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion. We propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty.
• Score: 99.47414073164656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge. Existing studies mainly focus on partial aspects of KACC. In order to promote thorough analyses for KACC abilities of models, we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty. Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links as well as dense entity graphs. Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark. For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples. The experimental results of existing methods demonstrate the challenges of our benchmark. The resource is available at https://github.com/thunlp/KACC.

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