Abstract
This paper presents KES (Knowledge Extraction and Summarization), a new knowledge-enhanced approach that builds a case memory out of episodic textual narratives. These narratives are considered as generated probabilistically by the structure of the task they describe. The task elements are then used to construct the structure of the case memory. The KES approach is illustrated with examples and an empirical evaluation of a real-world scenario of textual case-based interpretation for a technical domain. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Mustafaraj, E., Hoof, M., & Freisleben, B. (2007). Knowledge extraction and summarization for an application of textual case-based interpretation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4626 LNAI, pp. 517–531). Springer Verlag. https://doi.org/10.1007/978-3-540-74141-1_36
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