Abstract. 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 purpose, we conducted an experiment to evaluate variations of the algorithm for TCBR. We discuss the potential contribution of this algorithm to existing TCBR approaches. 1

Knowledge management (KM) systems manipulate organizational knowledge by storing and redistributing corporate memories that are acquired from the organization's members. In this paper, we introduce a textual casebased reasoning (TCBR) framework for KM systems that manipulates organizational knowledge embedded in artifacts (e.g., best practices, alerts, lessons learned). The TCBR approach acquires knowledge from human users (via knowledge elicitation) and from text documents (via knowledge extraction) using template-based information extraction methods, a subset of natural language, and a domain ontology. Organizational knowledge is stored in a case base and is distributed in the context of targeted processes (i.e., within external distribution systems). The knowledge artifacts in the case base have to be translated into the format of the external distribution systems. A domain ontology supports knowledge elicitation and extraction, storage of knowledge artifacts in a case base, and artifact translation.