Skip to main content

Web-Mining Defeasible Knowledge from Concessional Statements

  • Conference paper
  • First Online:
Graph-Based Representation and Reasoning (ICCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9717))

Included in the following conference series:

  • 854 Accesses

Abstract

Mining common-sense knowledge is a vital problem of artificial intelligence that forms the basis of various tasks, from information retrieval to robotics. There have been numerous initiatives to mine common-sense facts from unstructured data, more specifically, from Web texts. However, common-sense knowledge is typically not explicitly stated in the text, as it is considered to be obvious, self-evident, and thus shared between writer and reader. We argue that certain types of defeasible common-sense knowledge (i.e., knowledge that holds in most but not all cases), in particular, beliefs and stereotypes, tend to appear in text in a particular manner: they are not explicitly manifested, unless the speakers encounter a situation that runs in contrast to their defeasible common-sense assumptions. For example, if a speaker believes that Spain is a very warm country, she may express a surprise when it snows in Bilbao. We further argue that such conceptual contradictions correspond to the linguistic relation of concession (e.g., although Bilbao is in Spain, it is snowing there today) and we present a methodology for extracting defeasible common-sense beliefs (it is not common to snow in Spain) from Web data using concessive linguistic markers. We illustrate the methodology by mining beliefs about persons and we show that we are able to extract new information compared to existing common-sense knowledge bases.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://wordnetweb.princeton.edu/perl/webwn?s=gender.

  2. 2.

    Source: http://novrianfathi.blogspot.co.uk/.

  3. 3.

    http://stanfordnlp.github.io/CoreNLP/.

  4. 4.

    https://en.wikipedia.org/wiki/Portal:Contents/Categories.

  5. 5.

    Queried on 21.01.2016.

  6. 6.

    https://gate.d5.mpi-inf.mpg.de/webchild/.

  7. 7.

    As queried by google.com on Feb, 10th 2016.

References

  1. Davis, E.: Representations of commonsense knowledge. Morgan Kaufmann, Burlington (2014)

    Google Scholar 

  2. Fensel, D.: Ontologies - a silver bullet for knowledge management and electronic commerce. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  3. Grice, H.P.: Logic and conversation. In: Cole, P., Morgan, J. (eds.) Syntax and Semantics. Speech Acts, vol. 3, pp. 41–58. Academic Press, New York (1975)

    Google Scholar 

  4. Izutsu, M.N.: Contrast, concessive, and corrective: Toward a comprehensive study of opposition relations. J. Pragmatics 40(4), 646–675 (2008)

    Article  Google Scholar 

  5. Koons, R.: Defeasible reasoning. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2014). http://plato.stanford.edu/archives/spr2014/entries/reasoning-defeasible/, spring 2014 edn

  6. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 208–211. ACM (2008)

    Google Scholar 

  7. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M., Welling, J.: Never-ending learning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), pp. 2302–2310 (2015)

    Google Scholar 

  8. Moses, Y., Shoham, Y.: Belief as defeasible knowledge. Artif. intell. 64(2), 299–321 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  9. Nute, D.: Defeasible logic. In: Gabbay, D., Hogger, C., Robinson, J.A. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming. Nonmonotonic Reasoning and Uncertain Reasoning, vol. 3, pp. 355–395. Oxford University Press, New York (1994)

    Google Scholar 

  10. Robaldo, L., Miltsakaki, E.: Corpus-driven semantics of concession: Where do expectations come from? Dialogue & Discourse 5(1), 1–36 (2014)

    Google Scholar 

  11. Taboada, M., de los Ángeles Gómez-González, M.: Discourse markers and coherence relations: Comparison across markers, languages and modalities. Linguist. Hum. Sci. 6(1), 17–41 (2012)

    Google Scholar 

  12. Tandon, N., de Melo, G., Suchanek, F., Weikum, G.: Webchild: harvesting and organizing commonsense knowledge from the web. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 523–532. ACM (2014)

    Google Scholar 

  13. Von Ahn, L., Kedia, M., Blum, M.: Verbosity: a game for collecting common-sense facts. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 75–78. ACM (2006)

    Google Scholar 

  14. Winter, Y., Rimon, M.: Contrast and implication in natural language. J. Semant. 11(4), 365–406 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alina Petrova .

Editor information

Editors and Affiliations

A Appendix

A Appendix

Although-sentences extracted from top 50 Google search result snippets for the query although she is a woman, she (incomplete sentences are ignored):

Although she is a woman, she...

  1. 1.

    ...is fearless, skilled at riding, an excellent hunter, and a fine warrior.

  2. 2.

    ...is strong and capable of keeping his secrets.

  3. 3.

    ...is not seen as one in the book.

  4. 4.

    ...is equally as capable of doing farmwork as the men are.

  5. 5.

    ...has some influence, and warns Krogstad to avoid offending his superiors.

  6. 6.

    ...displays serious proof of having “balls.”?

  7. 7.

    ...is prepared to do it.

  8. 8.

    ...does not make any efforts to understand young Hazal’s sentiments

  9. 9.

    ...can endure the march as well as any man.

  10. 10.

    ...is the muscle of the family.

  11. 11.

    ...has the physique of a man with broad shoulders.

  12. 12.

    ...has the heart of a king and that the invasion by the Spanish Armada is still “foul.”

  13. 13.

    ...cannot bare working with women and this is reflected through her manners.

  14. 14.

    ...must demonstrate the “courage, ingenuity, and selflessness that is associated with Disney’s male heroes”.

  15. 15.

    ...is responsible for the tavern with her husband and she questions Falstaff without hesitation.

  16. 16.

    ...has nous within her.

  17. 17.

    ...has only a woman’s body and a woman’s charm without a woman’s heart.

  18. 18.

    ...is determined to surpass men.

  19. 19.

    ...is similar in many ways to Jack LaLanne.

  20. 20.

    ...has brought science, enlightenment, and “masculine” rationality to the “female” Orient.

  21. 21.

    ...is the dominant one in her relationship and is known for all her accomplishments in “The Family”.

  22. 22.

    ...is not seen as one.

  23. 23.

    ...believes it is only herself who can achieve her own fulfilment.

  24. 24.

    ...is more of a man than you.

  25. 25.

    ...is prepared to die.

  26. 26.

    ...holds the same power and authority as all the men who have ruled before her.

  27. 27.

    ...was born a boy.

  28. 28.

    ...is brave.

  29. 29.

    ...has never gone through the process of pregnancy and labor and delivery.

  30. 30.

    ...has more of an ambitious like a man compared to Duncan.

  31. 31.

    ...is very sensible and smart.

  32. 32.

    ...has a male power in her work.

  33. 33.

    ...fights to revive her ruined homeland

  34. 34.

    ...is hardly worth considering to be a sex object.

  35. 35.

    ...still “manned up”.

  36. 36.

    ...acts like man, so we can consider her a male.

  37. 37.

    ...will rule alone.

  38. 38.

    ...is equal to the occasion.

  39. 39.

    ...believes in chivalry.

  40. 40.

    ...has lofty aspirations.

  41. 41.

    ...acts much like a warrior, fighting alongside her Thenns like any other knight.

  42. 42.

    ...can do thing, which helps safe her family while her father loses the power as a family protector.

  43. 43.

    ...doesn’t use the typical features of women’s writing

  44. 44.

    ...isn’t good at cooking.

  45. 45.

    ...fights like a man.

  46. 46.

    ...has much confidence

  47. 47.

    ...is fighting to have high degree education

  48. 48.

    ...has not lost the wonder and playfulness of a child.

  49. 49.

    ...often can be more dependable and confident than men.

  50. 50.

    ...is still old yet mysterious and attractive to men.

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Petrova, A., Rudolph, S. (2016). Web-Mining Defeasible Knowledge from Concessional Statements. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-40985-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40984-9

  • Online ISBN: 978-3-319-40985-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics