GenericsKB: A Knowledge Base of Generic Statements

• URL: http://arxiv.org/abs/2005.00660v1
• Date: Sat, 2 May 2020 00:08:42 GMT
• Title: GenericsKB: A Knowledge Base of Generic Statements
• Authors: Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark
• Abstract summary: We present a new resource for the NLP community, namely a large (3.5M+ sentence) knowledge base of *generic statements* This is the first large resource to contain *naturally occurring* generic sentences, as opposed to extracted or crowdsourced triples. All GenericsKB sentences are annotated with their topical term, surrounding context (sentences), and a (learned) confidence.
• Score: 18.68800894936855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new resource for the NLP community, namely a large (3.5M+ sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon dioxide from the atmosphere", collected from multiple corpora. This is the first large resource to contain *naturally occurring* generic sentences, as opposed to extracted or crowdsourced triples, and thus is rich in high-quality, general, semantically complete statements. All GenericsKB sentences are annotated with their topical term, surrounding context (sentences), and a (learned) confidence. We also release GenericsKB-Best (1M+ sentences), containing the best-quality generics in GenericsKB augmented with selected, synthesized generics from WordNet and ConceptNet. In tests on two existing datasets requiring multihop reasoning (OBQA and QASC), we find using GenericsKB can result in higher scores and better explanations than using a much larger corpus. This demonstrates that GenericsKB can be a useful resource for NLP applications, as well as providing data for linguistic studies of generics and their semantics. GenericsKB is available at https://allenai.org/data/genericskb.

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