Managing Diagnostic Knowledge in Text Cases

A valuable source of field diagnostic information for equipment service resides in the text notes generated during service calls. Intelligent knowledge extraction from such textual information is a challenging task. The notes are characterized by misspelled words, incomplete information, cryptic technical terms, and non-standard abbreviations. In addition, very few of the total number of notes generated may be diagnostically useful. We present an approach for identifying diagnostically relevant notes from the many raw field service notes and information is presented in this paper. N-gram matching and supervised learning techniques are used to generate recommendations for the diagnostic significance of incoming service notes. These techniques have potential applications in generating relevant indices for textual CBR.