Skip to main content
Log in

Inference of isA commonsense knowledge with lexical taxonomy

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Commonsense knowledge is a crucial resource to help the machine understand the human world. However, the conventional methods of extracting commonsense knowledge with isA relation (or isA commonsense knowledge) from text corpora generally do not work well since commonsense knowledge is typically omitted in communication. In this paper, we mainly focus on the inference of isA commonsense knowledge (the definition of isA here to express a hypernym-hyponym relationship and we concentrate on whether the description of (s, isA, o) is correct based on this relationship, e.g., (mammal, isA, animal), (Hello Kitty, isA, cat)) with a special kind of knowledge graph: lexical taxonomy. Lexical and semantic features of terms are both extracted from three relationships including exclusive, compatible, andinclusive relationships then a simple but effective classification model is further utilized to predict whether isA commonsense holds or not. Besides, we implement our model on a lexical taxonomy: Probase. A series of comparative experiments prove the effectiveness of our approach with an accuracy of over 96%, and we infer 200k isA commonsense knowledge from 1 million new pairs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. It is worth noting that the task in this paper does not strictly distinguish between the InstanceOf relation and SubClassOf relation

  2. https://mathworld.wolfram.com/CommutativeDiagram.html

  3. https://concept.research.microsoft.com/Home/Download

References

  1. Tandon N, Varde AS, de Melo G (2018) Commonsense knowledge in machine intelligence. ACM SIGMOD Record 46(4):49–52

    Article  Google Scholar 

  2. Lee K, Cho H, Hwang S (2017) Gradable adjective embedding for commonsense knowledge. In: Pacific-asia conference on knowledge discovery and data mining. Springer, pp 814–827

  3. Lenat DB, Guha RV (1989) Building large knowledge-based systems; representation and inference in the Cyc project. Addison-Wesley, Longman Publishing Co Inc

    Google Scholar 

  4. Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41

    Article  Google Scholar 

  5. Von Ahn L, Kedia M, Blum M (2006) Verbosity: a game for collecting common-sense facts. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 75–78

  6. Herdaġdelen A, Baroni M (2012) Bootstrapping a game with a purpose for commonsense collection. ACM Transactions on Intelligent Systems and Technology (TIST) 3(4):59

    Google Scholar 

  7. Pasca M, Van Durme B (2007) What you seek is what you get: extraction of class attributes from query logs. In: IJCAI, vol-7, pp 2832–2837

  8. Fabian MS, Gjergji K, Weikum G et al (2007) Yago: a core of semantic knowledge unifying wordnet and wikipedia. In: 16th international world wide web conference, WWW, pp 697–706

  9. Tandon N, De Melo G, Weikum G (2014) Acquiring comparative commonsense knowledge from the web. In: AAAI, pp 166–172

  10. Tandon N, Hariman C, Urbani J, Rohrbach A, Rohrbach M, Weikum G (2016) Commonsense in parts: Mining part-whole relations from the web and image tags. In: AAAI, pp 243–250

  11. Wang G, Liu S, Wei F (2021) Weighted graph convolution over dependency trees for nontaxonomic relation extraction on public opinion information. Appl Intell, pp 1–15

  12. Wu W, Li H, Wang H, Zhu KQ (2012) Probase: a probabilistic taxonomy for text understanding. In: proceedings of the 2012 ACM SIGMOD international conference on management of data. ACM, pp 481–492

  13. Chen J, Hu Y, Liu J, Xiao Y, Jiang H (2019) Deep short text classification with knowledge powered attention. In: Proceedings of the AAAI conference on artificial intelligence, vol-33, pp 6252–6259

  14. Liu J, Wang M, Wang C, Liang J, Chen L, Jiang H, Xiao Y, Chen Y (2021) Learning term embeddings for lexical taxonomies. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 6410–6417

  15. Fallucchi F, Zanzotto FM (2010) Transitivity in semantic relation learning. In: Natural language processing and knowledge engineering (NLP-KE) international conference on. IEEE, pp 1–8, p 2010

  16. Fu R, Guo J, Qin B, Che W, Wang H, Liu T (2014) Learning semantic hierarchies via word embeddings. In: ACL, vol 1, pp 1199–1209

  17. Liang J, Yi Z, Xiao Y, Wang H, Wang W, Zhu P (2017) On the transitivity of hypernym-hyponym relations in data-driven lexical taxonomies. In: AAAI, pp 1185–1191

  18. Li P, Wang H, Zhu KQ, Wang Z, Wu X (2013) Computing term similarity by large probabilistic isa knowledge. In: proceedings of the 22nd ACM international conference on conference on information and knowledge management. ACM, pages 1401–1410

  19. Liang J, Xiao Y, Wang H, Yi Z, Wang W (2017) Probase+: inferring missing links in conceptual taxonomies. IEEE Trans Knowl Data Eng 29(6):1281–1295

    Article  Google Scholar 

  20. Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora, pp 539–545

  21. Suchanek FM, Kasneci G, Weikum G (2007) Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on world wide web. ACM, pp 697–706

  22. Li J, Wang C, He X, Zhang R, Gao M (2015) User generated content oriented chinese taxonomy construction. In: Asia-pacific web conference. Springer, pp 623–634

  23. Chen J, Wang A, Chen J, Xiao Y, Chu Z, Liu J, Liang J, Wang W (2019) Cn-probase: a data-driven approach for large-scale chinese taxonomy construction. In: 2019 IEEE 35th international conference on data engineering (ICDE). IEEE, pp 1706–1709

  24. Yaghoobzadeh Y, Schütze H (2015) Corpus-level fine-grained entity typing using contextual information. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 715–725

  25. Wang C, He X (2020) Birre: learning bidirectional residual relation embeddings for supervised hypernymy detection. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3630–3640

  26. Dash S, Chowdhury MFM, Gliozzo A, Mihindukulasooriya N, Fauceglia NR (2020) Hypernym detection using strict partial order networks. In: Proceedings of the conference on artificial intelligence. AAAI, vol 34, pp 7626–7633

  27. Yu C, Han J, Wang P, Song Y, Zhang H, Ng W, Shi S (2020) When hearst is not enough: improving hypernymy detection from corpus with distributional models. In: Conference on empirical methods in natural language processing. EMNLP, pp 6208–6217

  28. Wu T, Ling S, Qi G, Wang H (2014) Mining type information from chinese online encyclopedias. In: Joint international semantic technology conference. Springer, pp 213–229

  29. Kliegr T, Zamazal O (2016) Lhd 2.0: a text mining approach to typing entities in knowledge graphs. Journal of Web Semantics 39:47–61

    Article  Google Scholar 

  30. Chen HY, Lee CS, Liao KT, Lin SD (2018) Word relation auto encoder for unseen hypernym extraction using word embeddings. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 4834–4839

  31. Wang C, Fan Y, He X, Zhou A (2019) Predicting hypernym–hyponym relations for Chinese taxonomy learning. Knowl Inf Syst 58(3):585–610

    Article  Google Scholar 

  32. Zang LJ, Cao C, Cao YN, Wu YM, Cao CG (2013) A survey of commonsense knowledge acquisition. J Comput Sci Technol 28(4):689–719

    Article  MathSciNet  MATH  Google Scholar 

  33. Cambria E, Song Y, Wang H, Hussain A (2011) Isanette: a common and common sense knowledge base for opinion mining. In: 2011 IEEE 11th international conference on data mining workshops. IEEE, pp 315–322

  34. Grice HP (1975) Logic and conversation. In: Speech acts. Brill, pp 41–58

  35. Ramage D, Rafferty AN, Manning CD (2009) Random walks for text semantic similarity. In: Proceedings of the 2009 workshop on graph-based methods for natural language processing, Association for Computational Linguistics, pp 23–31

  36. Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378

    Article  Google Scholar 

  37. Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression, vol 398. Wiley, New York

    Book  MATH  Google Scholar 

  38. Yan X, Ge H, Yan Q (2006) Svm with rbf kernel and its application research. Computer Engineering and Design 27(11):1996–1997

    Google Scholar 

  39. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  40. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Technical report California Univ San Diego La Jolla zInst for Cognitive Science

  41. Bordes A, Usunier N, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: International conference on neural information processing systems, pp 2787–2795

  42. Sun Z, Deng ZH, Nie JY, Tang J (2019) Rotate: knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations, ICLR

  43. Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, NAACL-HLT, pp 4171–4186

  44. Trouillon T, Dance CR, Gaussier É, Welbl J, Riedel S, Bouchard G (2017) Knowledge graph completion via complex tensor factorization. J Mach Learn Res 18:1–38

    MathSciNet  MATH  Google Scholar 

  45. Borrego A, Ayala D, Hernández I, Rivero CR, Ruiz D (2021) Cafe: knowledge graph completion using neighborhood-aware features. Eng Appl Artif Intell 103:104302

    Article  Google Scholar 

  46. Feng J, Wei Q, Cui J, Chen J (2021) Novel translation knowledge graph completion model based on 2d convolution. Appl Intell, pp 1–10

  47. Wang H, Jiang S, Yu Z (2020) Modeling of complex internal logic for knowledge base completion. Appl Intell 50:3336–3349

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Key R&D Program of China under Grant 2018YFB1403200.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jingping Liu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Liu, J., Liu, J. et al. Inference of isA commonsense knowledge with lexical taxonomy. Appl Intell 53, 5290–5303 (2023). https://doi.org/10.1007/s10489-022-03680-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03680-4

Keywords

Navigation