Ontology-supported FAQ processing and ranking techniques

  • Authors:

  • Sheng-Yuan Yang;Fang-Chen Chuang;Cheng-Seen Ho

  • Affiliations:

  • Department of Electronic Engineering, National Taiwan University of Science and Technology, Taiwan, Republic of China and Department of Computer and Communication Engineering, St. John's Universit ...;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan, Republic of China;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan, Republic of China and Department of Electronic Engineering, Hwa Hsia Insti ...

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2007

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Abstract

This paper describes an FAQ system on the Personal Computer (PC) domain, which employs ontology as the key technique to pre-process FAQs and process user query. It is also equipped with an enhanced ranking technique to present retrieved, query-relevant results. Basically, the system bases on the wrapper technique to help clean, retrieve, and transform FAQ information collected from a heterogeneous environment and stores it in an ontological database. During retrieval of FAQs, the system trims irrelevant query keywords, employs either full keywords match or partial keywords match to retrieve FAQs, and removes conflicting FAQs before turning the final results to the user. Ontology plays the key role in all the above activities. To produce a more effective presentation of the search results, the system employs an enhanced ranking technique, which includes Appearance Probability, Satisfaction Value, Compatibility Value, and Statistic Similarity Value as four measures properly weighted to rank the FAQs. Our experiments show the system does improve precision rate and produces better ranking results. The proposed FAQ system manifests the following interesting features. First, the ontology-supported FAQ extraction from webpages can clean FAQ information by removing redundant data, restore missing data, and resolve inconsistent data. Second, the FAQs are stored in an ontology-directed internal format, which supports semantics-constrained retrieval of FAQs. Third, the ontology-supported natural language processing of user query helps pinpoint user's intent. Finally, the partial keywords match-based ranking method helps present user-most-wanted, conflict-free FAQ solutions for the user.