Document Type


Department or Administrative Unit

Computer Science

Publication Date



Often evidence from a single case does not reveal any suspicious patterns to aid investigations in forensic accounting and other forensic fields. In contrast, correlation of sets of evidence from several cases with suitable background knowledge may reveal suspicious patterns. Link Discovery (LD) has recently emerged as a promising new area for such tasks. Currently LD mostly relies on deterministic graphical techniques. Other relevant techniques are Bayesian probabilistic and causal networks. These techniques need further development to handle rare events. This paper combines first-order logic (FOL) and probabilistic semantic inference (PSI) to address this challenge. Previous research has shown this approach is computationally efficient and complete for statistically significant patterns. This paper shows that a modified method can be successful for discovering rare patterns. The method is illustrated with an example of discovery of suspicious patterns.


This is a post-peer-review, pre-copyedit version of an article published in KES 2003: Knowledge-Based Intelligent Information and Engineering Systems. The final authenticated version is available online at:


KES 2003: Knowledge-Based Intelligent Information and Engineering Systems


© Springer-Verlag Berlin Heidelberg 2003