Knowledge graphs (KGs ) contains an instance-level entity graph and an ontology-level concept graph. Recent studies reveal that jointly modeling of these two graphs could improve the understanding of each one. The completion processes on the concept graph and the entity graph can be further regarded as processes of knowledge abstractionand concretization. However, concept graphs in existing datasets are usually small and the links between concepts and entities are usually sparse, which cannot provide sufficient information for knowledge transfer between the two graphs. In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
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 URL
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Knowledge graphs (KGs
A comprehensive knowledge graph (KG
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Recent studies reveal that jointly modeling of these two graphs could improve the understanding of each one. The completion processes on the concept graph and the entity graph can be further regarded as processes of knowledge abstractionand concretization. However, concept graphs in existing datasets are usually small and the links between concepts and entities are usually sparse, which cannot provide sufficient information for knowledge transfer between the two graphs. In this paper
The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization
A
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,
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large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand
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,
A
as well as dense entity graphs
R
further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset
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