ConceptNet: a practical commonsense reasoning toolkit. ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. ConceptNet is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project — a World Wide Web based collaboration with over 14 000 authors

References in zbMATH (referenced in 31 articles )

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  1. Loureiro, Daniel; Mário Jorge, Alípio; Camacho-Collados, Jose: LMMS reloaded: transformer-based sense embeddings for disambiguation and beyond (2022)
  2. Koyyalagunta, Divya; Sun, Anna; Draelos, Rachel Lea; Rudin, Cynthia: Playing codenames with language graphs and word embeddings (2021)
  3. Loukachevitch, N. V.; Tikhomirov, M. M.; Parkhomenko, E. A.: Using embedding-based similarities to improve lexical resources (2021)
  4. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  5. Huang, Feicheng; Li, Zhixin; Wei, Haiyang; Zhang, Canlong; Ma, Huifang: Boost image captioning with knowledge reasoning (2020)
  6. Wang, Shaonan; Zhang, Jiajun; Wang, Haiyan; Lin, Nan; Zong, Chengqing: Fine-grained neural decoding with distributed word representations (2020)
  7. Aakur, Sathyanarayanan N.; Dias Moreira de Souza, Fillipe; Sarkar, Sudeep: Generating open world descriptions of video using common sense knowledge in a pattern theory framework (2019)
  8. Claudia Schon, Sophie Siebert, Frieder Stolzenburg: Using ConceptNet to Teach Common Sense to an Automated Theorem Prover (2019) arXiv
  9. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  10. Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Dhruv Batra, Devi Parikh: VQA: Visual Question Answering (2015) arXiv
  11. Poria, Soujanya; Cambria, Erik; Hussain, Amir; Huang, Guang-Bin: Towards an intelligent framework for multimodal affective data analysis (2015) ioport
  12. Bordes, Antoine; Glorot, Xavier; Weston, Jason; Bengio, Yoshua: A semantic matching energy function for learning with multi-relational data (2014)
  13. Dehdarbehbahani, Iman; Shakery, Azadeh; Faili, Heshaam: Semi-supervised word polarity identification in resource-Lean languages (2014) ioport
  14. Olsher, Daniel: Semantically-based priors and nuanced knowledge core for big data, social AI, and language understanding (2014) ioport
  15. Veale, Tony; Li, Guofu: Analogy as an organizational principle in the construction of large knowledge-bases (2014) ioport
  16. Zarri, Gian Piero: Sentiments analysis at conceptual level making use of the Narrative Knowledge Representation Language (2014) ioport
  17. Das, Shubhomoy; Moore, Travis; Wong, Weng-Keen; Stumpf, Simone; Oberst, Ian; McIntosh, Kevin; Burnett, Margaret: End-user feature labeling: supervised and semi-supervised approaches based on locally-weighted logistic regression (2013)
  18. Pal, Sankar K.; Banerjee, Romi: Context granulation and subjective-information quantification (2013)
  19. Zang, Liang-Jun; Cao, Cong; Cao, Ya-Nan; Wu, Yu-Ming; Cao, Cun-Gen: A survey of commonsense knowledge acquisition (2013)
  20. Erdem, Esra; Patoglu, Volkan: Applications of action languages in cognitive robotics (2012)

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