Interactive Text Graph Mining with a Prolog-based Dialog Engine

  • Paul TarauEmail author
  • Eduardo Blanco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12007)


On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document.

We reorganize dependency graphs to focus on the most relevant content elements of a sentence, integrate sentence identifiers as graph nodes and after ranking the graph we take advantage of the implicit semantic information that dependency links bring in the form of subject-verb-object, “is-a” and “part-of” relations.

Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document’s most relevant content elements.

The open-source code of the integrated system is available at


Logic-based dialog engine Graph-based natural language processing Dependency graphs query-driven salient sentence extraction Synergies between neural and symbolic text processing 



We are thankful to the anonymous reviewers of PADL’2020 for their careful reading and constructive suggestions.


  1. 1.
    Lierler, Y., Inclezan, D., Gelfond, M.: Action languages and question answering. In: Gardent, C., Retoré, C. (eds.) IWCS 2017–12th International Conference on Computational Semantics - Short papers, Montpellier, France, 19–22 September 2017. The Association for Computer Linguistics (2017)Google Scholar
  2. 2.
    Inclezan, D., Zhang, Q., Balduccini, M., Israney, A.: An ASP methodology for understanding narratives about stereotypical activities. TPLP 18(3–4), 535–552 (2018)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Mitra, A., Clark, P., Tafjord, O., Baral, C.: Declarative question answering over knowledge bases containing natural language text with answer set programming. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, pp. 3003–3010. AAAI Press (2019)Google Scholar
  4. 4.
    Inclezan, D.: RestKB: a library of commonsense knowledge about dining at a restaurant. In: Bogaerts, B., et al. (eds.) Proceedings 35th International Conference on Logic Programming (Technical Communications), Las Cruces, NM, USA, 20–25 September 2019. Volume 306 of Electronic Proceedings in Theoretical Computer Science, pp. 126–139. Open Publishing Association (2019)Google Scholar
  5. 5.
    Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017)Google Scholar
  6. 6.
    Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018)Google Scholar
  7. 7.
    Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 740–750. Association for Computational Linguistics (2014)Google Scholar
  8. 8.
    Adolphs, P., Xu, F., Li, H., Uszkoreit, H.: Dependency graphs as a generic interface between parsers and relation extraction rule learning. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS (LNAI), vol. 7006, pp. 50–62. Springer, Heidelberg (2011). Scholar
  9. 9.
    Choi, J.D.: Deep dependency graph conversion in English. In: Proceedings of the 15th International Workshop on Treebanks and Linguistic Theories, TLT 2017, Bloomington, IN, pp. 35–62 (2017)Google Scholar
  10. 10.
    Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22(1), 457–479 (2004)CrossRefGoogle Scholar
  11. 11.
    Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain, July 2004Google Scholar
  12. 12.
    Mihalcea, R., Tarau, P.: An algorithm for language independent single and multiple document summarization. In: Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), Korea, October 2005Google Scholar
  13. 13.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)Google Scholar
  14. 14.
    Fellbaum, C.: WordNet, An Electronic Lexical Database. The MIT Press, Cambridge (1998)CrossRefGoogle Scholar
  15. 15.
    Schulte, C.: Programming constraint inference engines. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 519–533. Springer, Heidelberg (1997). Scholar
  16. 16.
    Denecker, M., Kakas, A.: Abduction in logic programming. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2407, pp. 402–436. Springer, Heidelberg (2002). Scholar
  17. 17.
    Schaub, T., Woltran, S.: Special issue on answer set programming. KI 32(2–3), 101–103 (2018)Google Scholar
  18. 18.
    Olson, C., Lierler, Y.: Information extraction tool Text2ALM: from narratives to action language system descriptions. In: Bogaerts, B., et al. (eds.) Proceedings 35th International Conference on Logic Programming (Technical Communications), Las Cruces, NM, USA, 20–25 September 2019. Volume 306 of Electronic Proceedings in Theoretical Computer Science, pp. 87–100. Open Publishing Association (2019)Google Scholar
  19. 19.
    Krapivin, M., Autayeu, A., Marchese, M.: Large dataset for keyphrases extraction. Technical report DISI-09-055, DISI, Trento, Italy, May 2008Google Scholar
  20. 20.
    Wielemaker, J., Schrijvers, T., Triska, M., Lager, T.: SWI-Prolog. Theory Pract. Logic. Program. 12, 67–96 (2012)zbMATHGoogle Scholar
  21. 21.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the 11th International Conference on World Wide Web, WWW 2002, pp. 517–526. ACM, New York (2002)Google Scholar
  22. 22.
    Haveliwala, T., Kamvar, S., Jeh, G.: An analytical comparison of approaches to personalizing PageRank. Technical report 2003–35, Stanford InfoLab, June 2003Google Scholar
  23. 23.
    Tarau, P., Blanco, E.: Dependency-based text graphs for keyphrase and summary extraction with applications to interactive content retrieval. arXiv abs/1909.09742 (2019)Google Scholar
  24. 24.
    de Marneffe, M.C., et al.: Universal Stanford dependencies: a cross-linguistic typology. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), Reykjavik, Iceland, pp. 4585–4592. European Languages Resources Association (ELRA), May 2014Google Scholar
  25. 25.
    Choi, J.D., Palmer, M.: Transition-based semantic role labeling using predicate argument clustering. In: Proceedings of the ACL 2011 Workshop on Relational Models of Semantics. RELMS 2011, Stroudsburg, PA, USA, pp. 37–45. Association for Computational Linguistics (2011)Google Scholar
  26. 26.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)Google Scholar
  27. 27.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998). Scholar
  28. 28.
    Mihalcea, R.F., Radev, D.R.: Graph-Based Natural Language Processing and Information Retrieval, 1st edn. Cambridge University Press, New York (2011)CrossRefGoogle Scholar
  29. 29.
    Nenkova, A., McKeown, K.R.: A survey of text summarization techniques. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 43–76. Springer, Boston (2012). Scholar
  30. 30.
    Allahyari, M., et al.: Text summarization techniques: a brief survey. CoRR abs/1707.02268 (2017)Google Scholar
  31. 31.
    Stevenson, M., Greenwood, M.: Dependency pattern models for information extraction. Res. Lang. Comput. 7(1), 13–39 (2009)CrossRefGoogle Scholar
  32. 32.
    Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, Stroudsburg, PA, USA, pp. 724–731. Association for Computational Linguistics (2005)Google Scholar
  33. 33.
    Peng, Y., Gupta, S., Wu, C., Shanker, V.: An extended dependency graph for relation extraction in biomedical texts. In: Proceedings of BioNLP 15, pp. 21–30. Association for Computational Linguistics (2015)Google Scholar
  34. 34.
    Mihalcea, R., Tarau, P., Figa, E.: PageRank on semantic networks, with application to word sense disambiguation. In: Proceedings of the 20st International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland, August 2004Google Scholar
  35. 35.
    Li, W., Zhao, J.: TextRank algorithm by exploiting wikipedia for short text keywords extraction. In: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 683–686 (2016)Google Scholar
  36. 36.
    Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 233–242. ACM, New York (2007)Google Scholar
  37. 37.
    Bos, J.: Open-domain semantic parsing with boxer. In: Megyesi, B. (ed.) Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, 1–13 May 2015, pp. 301–304. Institute of the Lithuanian Language, Vilnius, Linköping University Electronic Press/ACL (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of North TexasDentonUSA

Personalised recommendations