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.