The growing size of electronically available text corpora like companies’ intranets or the WWW has made information access a hot topic within Computational Linguistics. Despite the success of statistical or keyword based methods, deeper Knowledge Representation (KR) techniques along with “inference” are often mentioned as mandatory, e.g. within the Semantic Web context, to enable e.g. better query answering based on “semantical” information. In this paper we try to contribute to the open question how to operationalize semantic information on a larger scale. As a basis we take the frame structures of the Berkeley FrameNet II project, which is a structured dictionary to explain the meaning of words from a lexicographic perspective. Our main contribution is a transformation of the FrameNet II frames into the answer set programming paradigm of logic programming. Because a number of different reasoning tasks are subsumed under “inference” in the context of natural language processing, we emphasize the flexibility of our transformation. Together with methods for automatic annotation of text documents with frame semantics which are currently developed at various sites, we arrive at an infrastructure that supports experimentation with semantic information access as is currently demanded for.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element