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Node-First Causal Network Extraction for Trend Analysis Based on Web Mining

  • Hideki Kawai
  • Katsumi Tanaka
  • Kazuo Kunieda
  • Keiji Yamada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6882)

Abstract

In this paper, we propose a node-first causal extraction method for trend analysis. Recently, it has become more important for business leaders, politicians and academics to understand broader and longer environmental trends because of the need to develop better strategies for dealing with current and future issues. Trend analysis is often utilized to identify key factors in political, economical, social and technological trends. We propose a web mining framework that can extract a causal network of key factors underlying macro trends related to a user’s interest. The main idea can be described as ”node-first” approach, which recursively identifies key factors relevant to a user’s query, then verifies causal relations between key factors. As the result of experiment, we demonstrate high precision of key factor identification (P@100 = 0.76) and causality verifications (F-value = 0.74).

Keywords

Causal Relation Trend Analysis Causal Network Causal Loop Diagram Causal Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abe, H., Suzuki, A., Etohc, M., Sibagaki, S., Koike, S.: Towards Systematic Innovation Methods: Innovation Support Technology that Integrates Business Modeling, Roadmapping and Innovation Architecture. In: Proc. PICMET 2008 Conference, Cape Town, South Africa, pp. 2141–2149 (July 2008)Google Scholar
  2. 2.
    Araúz, P.L., Faber, P.: Natural and contextual constraints for domain-specific relations. In: The Workshop Semantic Relations, Theory and Applications, Valletta, Malta, pp. 12–17 (May 2010)Google Scholar
  3. 3.
    Auger, A.: Capturing and Modeling Domain Knowledge Using Natural Language Processing Techniques. In: 10th International Command and Control Research and Technology Symposium (ICCRTS 2005) (June 2005)Google Scholar
  4. 4.
    Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., Yu, Y.: Optimizing Web Search Using Social Annotations. In: Proc. the 16th International World Wide Web Conference (WWW 2007), Alberta, Canada, pp. 501–510 (May 2007)Google Scholar
  5. 5.
    Belausteguigoitia, J.C.: Causal Chain Analysis and Root Causes: The GIWA Approach. Ambio 33(1-2), 7–12 (2004)CrossRefGoogle Scholar
  6. 6.
    Blanco, E., Castell, N., Moldovan, D.: Causal Relation Extraction. In: The 6th International Conference on Language Resources and Evaluation (LREC 2008), Marrakech, Morocco, pp. 310–313 (May 2008)Google Scholar
  7. 7.
    Burandt, S.: Effects of an Educational Scenario Exercise on Participants Competencies of Systemic Thinking. Journal of Social Sciences 7(1), 51–62 (2011)CrossRefGoogle Scholar
  8. 8.
    Chang, D.S., Choi, K.S.: Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities. Information Processing and Management 42, 662–678 (2006)CrossRefGoogle Scholar
  9. 9.
    Chao, K.: A New Look at the Cross-Impact Matrix and its Application in Futures Studies. Journal of Futures Studies 12(4), 45–52 (2008)Google Scholar
  10. 10.
    Girju, R., Moldovan, D.: Mining Answers for Causation Questions. In: The AAAI Spring Symposium on Mining Answers from Texts and Knowledge Bases, Palo Alto, CA, USA, pp. 15–25 (March 2002)Google Scholar
  11. 11.
    Glenn, J.C., Gordon, T.J.: Futures Research Methodology Version 3.0. Amer Council for the United Nations, Washington, D.C, USA (2009)Google Scholar
  12. 12.
    Ha, H., Coghill, K.: E-Government in Singapore – A Swot and Pest Analysis. Asia-Pacific Social Science Review 6(2), 103–130 (2006)Google Scholar
  13. 13.
    Higashinaka, R., Isozaki, H.: Automatically Acquiring Causal Expression Patterns from Relation-annotated Corpora to Improve Question Answering for why- Questions. ACM Transactions on Asian Language Information Processing 7(2) (2008)Google Scholar
  14. 14.
    Inui, T., Inui, K., Matsumoto, Y.: Acquiring Causal Knowledge from Text Using the Connective Marker tame. ACM Transactions on Asian Language Information Processing 4(4), 435–474 (2005)CrossRefGoogle Scholar
  15. 15.
    Inui, T., Okumura, M.: Investigating the Characteristics of Causal Relations in Japanese Text. In: The Workshop on Frontiers in Corpus Annotation II: Pie in the Sky, Stroudsburg, PA, USA, pp. 37–44 (June 2005)Google Scholar
  16. 16.
    Ishii, H., Ma, Q., Yoshikawa, M.: An incremental method for causal network construction. In: The 11th International Conference on Web-Age Information Management (WAIM 2010), Jiuzhaigou, China, pp. 495–506 (July 2010)Google Scholar
  17. 17.
    Jafari, M., Amiri, R.H., Bourouni, A.: An Interpretive Approach to Drawing Weighted and Most Frequent Causal Loop Diagram using ELECTRE III and SUBDUE Methods. International Journal of Intelligent Information Technology Application 2(3), 116–120 (2009)Google Scholar
  18. 18.
    Joshi, S., Pangaonkar, M., Seethakkagari, S., Mazlack, L.J.: Lexico-Syntactic Causal Pattern Text Mining. In: The 14th WSEAS International Conference on Computers, Corfu Island, Greece, pp. 446–452 (July 2010)Google Scholar
  19. 19.
    Khoo, C.S.G., Kornfilt, J., Oddy, R.N., Myaeng, S.H.: Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing. Literary and Linguistic Computing 13(4), 177–186 (1998)CrossRefGoogle Scholar
  20. 20.
    Peng, G.C.A., Nunes, M.B.: Using PEST Analysis as a Tool for Refining and Focusing Contexts for Information Systems Research. In: Proc. The 6th European Conference on Research Methodology for Business and Management Studies (ECRM 2007), Lisbon, Portugal, pp. 229–237 (July 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hideki Kawai
    • 1
  • Katsumi Tanaka
    • 2
  • Kazuo Kunieda
    • 1
  • Keiji Yamada
    • 1
  1. 1.NEC C & C Innovation Research LaboratoriesIkoma cityJapan
  2. 2.Graduate School of InfomaticsKyoto UniversityKyotoJapan

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