Believing Finite-State Cascades in Knowledge-Based Information Extraction

Authors:
Benjamin Adrian;Andreas Dengel
Affiliations:
Knowledge Management Department, DFKI, Kaiserslautern, Germany;Knowledge Management Department, DFKI, Kaiserslautern, Germany and CS Department, University of Kaiserslautern, Kaiserslautern, Germany
Venue:
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Year:
2008

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Abstract

Common information extraction systems are built upon regular extraction patterns and finite-state transducers for identifying relevant bits of information in text. In traditional systems a successful pattern match results in populating spreadsheet-like templates formalizing users' information demand. Many IE systems do not grade extraction results along a real scale according to correctness or relevance. This leads to difficult management of failures and missing or ambiguous information.The contribution of this work is applying beliefof Dempster-Shafer's A Mathematical Theory of Evidencefor grading IE results that are generated by probabilistic FSTs. This enhances performance of matching uncertain information from text with certain knowledge in knowledge bases. The use of beliefincreases precision especially in modern ontology-based information extraction systems.