# ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities

arXiv: https://arxiv.org/abs/2104.02137 · HTML: https://mlsanity.com/paper/2104.02137/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 115 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [Commonsense Knowledge Mining from Pretrained Models](https://mlsanity.com/paper/1909.00505/) (2019)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension](https://mlsanity.com/paper/1805.06975/) (2018)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [GenericsKB: A Knowledge Base of Generic Statements](https://mlsanity.com/paper/2005.00660/) (2020)
- [Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings](https://mlsanity.com/paper/1902.00913/) (2019)
- [On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification](https://mlsanity.com/paper/2004.12617/) (2020)
- [Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](https://mlsanity.com/paper/2007.00849/) (2020)
- [Constructing Narrative Event Evolutionary Graph for Script Event Prediction](https://mlsanity.com/paper/1805.05081/) (2018)
- [Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora](https://mlsanity.com/paper/1806.03191/) (2018)
- [Commonsense Properties from Query Logs and Question Answering Forums](https://mlsanity.com/paper/1905.10989/) (2019)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)
- [DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge](https://mlsanity.com/paper/2101.00154/) (2021)

## Built on by (most cited first, 28 of 28)

- [A Comprehensive Study of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2401.01286/) (2024)
- [Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://mlsanity.com/paper/2306.06427/) (2023)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models](https://mlsanity.com/paper/2210.16433/) (2022)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://mlsanity.com/paper/2505.23628/) (2025)
- [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://mlsanity.com/paper/2305.05191/) (2023)
- [Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints](https://mlsanity.com/paper/2305.19068/) (2023)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph](https://mlsanity.com/paper/2311.09174/) (2023)
- [Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset](https://mlsanity.com/paper/2109.07679/) (2021)
- [AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation](https://mlsanity.com/paper/2402.10646/) (2024)
- [Large Knowledge Model: Perspectives and Challenges](https://mlsanity.com/paper/2312.02706/) (2023)
- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge](https://mlsanity.com/paper/2112.08583/) (2021)
- [PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population](https://mlsanity.com/paper/2210.07988/) (2022)
- [Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce](https://mlsanity.com/paper/2205.10843/) (2022)
- [Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://mlsanity.com/paper/2305.05921/) (2023)
- [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://mlsanity.com/paper/2310.12011/) (2023)
- [On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions](https://mlsanity.com/paper/2406.10885/) (2024)
- [Automatic Extraction of Relationships among Motivations, Emotions and Actions from Natural Language Texts](https://mlsanity.com/paper/2408.00966/) (2024)
- [Are All Steps Equally Important? Benchmarking Essentiality Detection of Events](https://mlsanity.com/paper/2210.04074/) (2022)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://mlsanity.com/paper/2312.06053/) (2023)
- [Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models](https://mlsanity.com/paper/2606.24841/) (2026)

## Abstract (arXiv)

Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet), selectional preference (SP) knowledge only relies on statistical distribution over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpus with modern tools. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transfer their knowledge to new events, we propose a conceptualization module to significantly boost the coverage of ASER. In total, ASER contains 648 million edges between 438 million eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. All the collected data, APIs, and tools are available at https://github.com/HKUST-KnowComp/ASER.