CGExtract: Towards Extraction of Conceptual Graphs from Controlled English
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TL;DR
The CGExtract prototype is described, which approaches the subject by integration of Parasite, an already existing component for NL analysis and Understanding (NLU), and focuses on semantic KB consistency rather than on NL analysis.
Abstract
Abstract. Extraction of formal knowledge specications from Natu-ral Language (NL) text is a challenging research area. Currently the task is considered feasible for restricted NL input only. A number of CG researchers approached the problem, applying Sowa's algorithm for analysis of NL input by joins of canonical graphs. This paper summa-rizes the state of the art and describes the CGExtract prototype, which approaches the subject by integration of Parasite, an already existing component for NL analysis and Understanding (NLU). This powerful NLU machine produces a logical form and a model for each syntacti-cally correct sentence and processes coreferences in extended discourse of several sentences. Given an initial type hierarchy and relevant lexi-con information, CGExtract constructs new KB graphs corresponding to the input text and checks whether a new graph is in contradiction with the already existing KB graphs and/or yields loop denitions. The novelty of our approach is that it focuses on semantic KB consistency rather than on NL analysis. The CGWorld workbench [4] supports the user interface of CGExtract. 1