mlsanityData to 14 Oct 2026

ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

arXiv 1612.03975 ↗First posted 12 Dec 2016cs.CLcited by 1,067

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Cited by 93 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.

Key facts. Cited by 1,067 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Dec 2016.

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Citing papers per year: 2021: 190; 2022: 241; 2023: 229; 2024: 195; 2025: 136; 2026: 76.

1,067 papers.

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  1. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  2. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  3. 2023The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"cited by 407
  4. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  5. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  6. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 312
  7. 2021An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQAcited by 301
  8. 2022Post-hoc Concept Bottleneck Modelscited by 293
  9. 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
  10. 2021Surface Form Competition: Why the Highest Probability Answer Isn't Always Rightcited by 263
  11. 2022Reasoning with Language Model Prompting: A Surveycited by 252
  12. 2025CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillationcited by 225
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  1. 2022Unified Structure Generation for Universal Information Extractioncited by 221
  2. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 217
  3. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 214
  4. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  5. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 203
  6. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  7. 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answeringcited by 171
  8. 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 168
  9. 2022Deep Bidirectional Language-Knowledge Graph Pretrainingcited by 156
  10. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 150
  11. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  12. 2021Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Modelscited by 132
  13. 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 126
  14. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
  15. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  16. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  17. 2022From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Modelscited by 115
  18. 2021Are Convolutional Neural Networks or Transformers more like human vision?cited by 109
  19. 2022CommonsenseQA 2.0: Exposing the Limits of AI through Gamificationcited by 109
  20. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  21. 2022PromptCap: Prompt-Guided Task-Aware Image Captioningcited by 104
  22. 2021UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmarkcited by 98
  23. 2023PRODIGY: Enabling In-context Learning Over Graphscited by 98
  24. 2023Concept-based Explainable Artificial Intelligence: A Surveycited by 98
  25. 2021Learning to Predict Visual Attributes in the Wildcited by 96
  26. 2022Retrieval Augmented Visual Question Answering with Outside Knowledgecited by 94
  27. 2022SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Modelscited by 91
  28. 2021CEM: Commonsense-aware Empathetic Response Generationcited by 89
  29. 2022MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversationcited by 85
  30. 2023Crawling the Internal Knowledge-Base of Language Modelscited by 85
  31. 2023Modular Deep Learningcited by 83
  32. 2022K-LITE: Learning Transferable Visual Models with External Knowledgecited by 79
  33. 2023Boosting Language Models Reasoning with Chain-of-Knowledge Promptingcited by 77
  34. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 77
  35. 2022Extracting Cultural Commonsense Knowledge at Scalecited by 76
  36. 2023Language models are not naysayers: An analysis of language models on negation benchmarkscited by 74
  37. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  38. 2023Graph Neural Prompting with Large Language Modelscited by 64

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