›› 2010, Vol. 25 ›› Issue (5): 1055-1070.doi: 10.1007/s11390-010-1083-6

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence • Previous Articles     Next Articles

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Chaveevan Pechsiri1 and Rapepun Piriyakul2   

  1. 1. Department of Information Technology, Dhurakij Pundit University, Bangkok, Thailand;
    2. Department of Computer Science, Ramkumheang University, Bangkok, Thailand
  • Received:2008-12-10 Revised:2010-02-02 Online:2010-09-01 Published:2010-09-01
  • About author:
    Chaveevan Pechsiri holds the Bachelor's degree of science in food science and technology from Kasetsart University, Thailand, the Master's degree in food science and the Master's degree in computer science both from Mississippi State University, USA, and the D.Eng. degree in computer engineering from Kasetsart University, Thailand. She is an associate professor at Dhurakij Pundit University, Thailand and her research interest is natural language processing.
    Rapepun Piriyakul is currently an assistant professor at Ramkhumhaeng University, Thailand. She received the Bachelor's degree in mathematics from Chulalongkorn University, the Master's degree in applied statistics from National Institute of Development Administration, and the D.Eng. degree in computer engineering from Kasetsart University, Thailand. Her research interest is applied analytical statistics in computer engineering.
  • Supported by:

    Supported by the Thai Research Fund under Grant No. MRG5280094.

Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers. All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation knowledge graph construction is 90% based on expert judgments whereas the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively.

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