Statistical relational data integration for information extraction

Authors:
Mathias Niepert
Affiliations:
Department of Computer Science & Engineering, University of Washington, Seattle, WA
Venue:
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
Year:
2013

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Abstract

These lecture notes provide a brief overview of some state of the art large scale information extraction projects. Consequently, these projects are related to current research activities in the semantic web community. The majority of the learning algorithms developed for these information extraction projects are based on the lexical and syntactical processing of Wikipedia and large web corpora. Due to the size of the processed data and the resulting intractability of the associated inference problems existing knowledge representation formalism are often inadequate for the task. We will present recent advances in combining tractable logical and probabilistic models that bring statistical language processing and rule-based approaches closer together. With these lecture notes we hope to convince the attendees that there are numerous synergies and research agendas that can arise when uncertainty-based data-driven research meets rule-based schema-driven research. We also describe certain theoretical and practical advances in making probabilistic inference scale to very large problems.