GenericsKB: A Knowledge Base of Generic Statements

TL;DR

GenericsKB is a large, high-quality knowledge base of naturally occurring generic statements, enhancing NLP tasks and reasoning by providing semantically rich, annotated generic sentences collected from multiple sources.

Contribution

This work introduces GenericsKB, the first large-scale resource of naturally occurring generic sentences with annotations, improving reasoning and NLP applications over previous triple-based datasets.

Findings

Using GenericsKB improves multihop reasoning performance.

GenericsKB provides higher-quality generics than larger, less specific corpora.

The resource benefits NLP applications and linguistic research.

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.