Investigating Graphs in Textual Case-Based Reasoning

Textual case-based reasoning (TCBR) provides the ability to reason with domain-specific knowledge when experiences exist in text. Ideally, we would like to find an inexpensive way to automatically, efficiently, and accurately represent textual documents as cases. One of the challenges, however, is that current automated methods that manipulate text are not always useful because they are either expensive (based on natural language processing) or they do not take into account word order and negation (based on statistics) when interpreting textual sources. Recently, Schenker et al. [1] introduced an algorithm to convert textual documents into graphs that conserves and conveys the order and structure of the source text in the graph representation. Unfortunately, the resulting graphs cannot be used as cases because they do not take domain knowledge into consideration. Thus, the goal of this study is to investigate the potential benefit, if any, of this new algorithm to TCBR. For this purpos...

• Type: Conference
• Year: 2004
• Where: EWCBR
• Authors: Colleen Cunningham, Rosina Weber, Jason M. Proctor, Caleb Fowler, Michael Murphy
• Keywords: Artificial Intelligence, EWCBR 2004, Textual, Textual Case-based Reasoning, Textual Documents