Exploring optimization of semantic relationship graph for multi-relational Bayesian classification

Hailiang Chen, Hongyan Liu, Jiawei Han, Xiaoxin Yin, Jun He

Research output: Contribution to journalArticlepeer-review


In recent years, there has been growing interest in multi-relational classification research and application, which addresses the difficulties in dealing with large relation search space, complex relationships between relations, and a daunting number of attributes involved. Bayesian Classifier is a simple but effective probabilistic classifier which has been shown to be able to achieve good results in most real world applications. Existing works for multi-relational Naïve Bayes classifier mainly focus on how to extend traditional flat Naïve Bayes classification method to multi-relational environment. In this paper, we look into issues concerned with how to increase the accuracy of multi-relational Bayesian classifier but still retain its efficiency. We develop a Semantic Relationship Graph (SRG) to describe the relationship between multiple tables and guide the search within relation space. Afterwards, we optimize the Semantic Relationship Graph by avoiding undesirable joins between relations and eliminating unnecessary attributes and relations. The experimental study on the real-world and synthetic databases shows that the proposed optimizing strategies make the multi-relational Naïve Bayesian classifier achieve improved accuracy by sacrificing a small amount of running time.

Original languageEnglish (US)
Pages (from-to)112-121
Number of pages10
JournalDecision Support Systems
Issue number1
StatePublished - Jan 2009


  • Depth-first
  • Feature selection
  • Multi-relational classification
  • Naïve Bayesian classification
  • Semantic relationship graph
  • Width-first

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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