GenericsKB: A Knowledge Base of Generic Statements

By Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark. 2020

77 citations recorded by the source corpus.

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 this https URL.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus (2021, background): “There have been many attempts to capture such implicit knowledge vie common sense knowledge bases (Speer and Havasi, 2012; Sap et al., 2019a; Bhakthavatsalam et al., 2020).”
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021, background): “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).”
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021, methodology): “, 2017), the most widely used knowledge graph in commonsense reasoning tasks, and GenericsKB (Bhakthavatsalam et al., 2020), which expresses knowledge in the form of natural language sentences and has gained increasing usage.”
Birds Have Four Legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models (2020, methodology): “swer this question, we further collected training sentences from the GenericsKB corpus (Bhakthavatsalam et al., 2020).”
Knowledge-Augmented Methods for Natural Language Processing (2023, background): “…knowledge resources, such as ASER (Zhang et al., 2021), AscentKB (Nguyen et al., 2021), COMET-ATOMIC2020 (Hwang et al., 2021), and Generic-sKB (Bhakthavatsalam et al., 2020), which provide us with event-centric, large-scale, neural-symbolic, semi-structured ways to access and model…”
Differentiable Open-Ended Commonsense Reasoning (2020, methodology): “We use the GenericsKB-Best corpus (Bhakthavatsalam et al., 2020) as the main knowledge source.”
REV: Information-Theoretic Evaluation of Free-Text Rationales (2022, methodology): “To evaluate such rationales, we randomly select 250 gold labels in ECQA and extract their related sentences from a large-scale knowledge base—GenericsKB (Bhakthavatsalam et al., 2020).”
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer (2022, methodology): “As an ablation study, we explore other text corpora such as GenericsKB (Bhakthavatsalam et al., 2020), Wiki103 (Mer-”