Abstract
The LILOG system under development at IBM Germany is a text understanding system with a question/answering component for proving successful text processing. The texts under investigation are non-technical texts understandable for ‘normal people’ with commonsense knowledge. As well known in AI, text understanding systems require background knowledge for dealing with tacit and implicit textual information; in our case, this background knowledge has to be a kind of commonsense knowledge. The LILOG project works with the logical paradigm for reconstructing models of the world as the relevant domain of discourse. The modeling itself is oriented towards both the linguistic requirements and the demands resulting from tractability aspects of inference.
In this paper we characterize the texts under investigation and sketch the relevant features of the logical language for the modeling task. We will present examples of linguistic requirements and discuss some features of the model already implemented. The lesson learned from these works is that ‘classical’ knowledge acquisition techniques and methodologies developed for socalled expert systems are not suitable for commonsense modeling with underspecified tasks in mind, as in text understanding systems for nontechnical texts.
On the basis of these experiences a new methodology is sketched and discussed. Examples of tools for supporting this undertaking are given.
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Klose, G., von Luck, K. (1991). The background knowledge of the LILOG system. In: Herzog, O., Rollinger, CR. (eds) Text Understanding in LILOG. Lecture Notes in Computer Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54594-8_75
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DOI: https://doi.org/10.1007/3-540-54594-8_75
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