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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Belausteguigoitia, J.C.: Causal Chain Analysis and Root Causes: The GIWA Approach. Ambio 33(1-2), 7–12 (2004)
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)
Burandt, S.: Effects of an Educational Scenario Exercise on Participants Competencies of Systemic Thinking. Journal of Social Sciences 7(1), 51–62 (2011)
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)
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)
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)
Glenn, J.C., Gordon, T.J.: Futures Research Methodology Version 3.0. Amer Council for the United Nations, Washington, D.C, USA (2009)
Ha, H., Coghill, K.: E-Government in Singapore – A Swot and Pest Analysis. Asia-Pacific Social Science Review 6(2), 103–130 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kawai, H., Tanaka, K., Kunieda, K., Yamada, K. (2011). Node-First Causal Network Extraction for Trend Analysis Based on Web Mining. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-23863-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23862-8
Online ISBN: 978-3-642-23863-5
eBook Packages: Computer ScienceComputer Science (R0)