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Ferrucci, D. and Lally, A. (2004) Uima: An Architectural Approach to Unstructured Information Processing in the Corporate Research Environment. Natural Language Engineering, 10, 327-348.
http://dx.doi.org/10.1017/S1351324904003523

has been cited by the following article:

  • TITLE: Let Some Unforeseen Knowledge Emerge from Heterogeneous Documents

    AUTHORS: Maria Teresa Pazienza, Armando Stellato, Andrea Turbati

    KEYWORDS: Computing Methodologies, Knowledge Representation and Reasoning, Information Extraction

    JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.6, May 12, 2016

    ABSTRACT: Data production and exchange on the Web grows at a frenetic speed. Such uncontrolled and exponential growth pushes for new researches in the area of information extraction as it is of great interest and can be obtained by processing data gathered from several heterogeneous sources. While some extracted facts can be correct at the origin, it is not possible to verify that correlations among the mare always true (e.g., they can relate to different points of time). We need systems smart enough to separate signal from noise and hence extract real value from this abundance of content accessible on the Web. In order to extract information from heterogeneous sources, we are involved into the entire process of identifying specific facts/events of interest. We propose a gluing architecture, driving the whole knowledge acquisition process, from data acquisition from external heterogeneous resources to their exploitation for RDF trip lification to support reasoning tasks. Once the extraction process is completed, a dedicated reasoner can infer new knowledge as a result of the reasoning process defined by the end user by means of specific inference rules over both extracted information and the background knowledge. The end user is supported in this context with an intelligent interface allowing to visualize either specific data/concepts, or all information inferred by applying deductive reasoning over a collection of data.