18,248 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
ArCo: the Italian Cultural Heritage Knowledge Graph
ArCo is the Italian Cultural Heritage knowledge graph, consisting of a
network of seven vocabularies and 169 million triples about 820 thousand
cultural entities. It is distributed jointly with a SPARQL endpoint, a software
for converting catalogue records to RDF, and a rich suite of documentation
material (testing, evaluation, how-to, examples, etc.). ArCo is based on the
official General Catalogue of the Italian Ministry of Cultural Heritage and
Activities (MiBAC) - and its associated encoding regulations - which collects
and validates the catalogue records of (ideally) all Italian Cultural Heritage
properties (excluding libraries and archives), contributed by CH administrators
from all over Italy. We present its structure, design methods and tools, its
growing community, and delineate its importance, quality, and impact
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