196 research outputs found
Graphene: A Context-Preserving Open Information Extraction System
We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.Comment: 27th International Conference on Computational Linguistics (COLING
2018
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING
2018
Evolution in the Ontology Based Knowledge Management Systems
An ontology-based knowledge management system uses an ontology to represent explicit specification of a business domain and to serve as a backbone for providing and searching for knowledge sources. But, dynamically changing business environment implies changes in the conceptualisation of a business domain that are reflected on the underlying domain ontologies. Consequently, these changes have effects on the performance and validity of the KM system. In this paper we make an analysis of the problems induced by using not-evolved ontologies and present an approach for enabling consistency of the description of knowledge sources in an ontology-based KM system in the case of changes in the domain ontology. This approach is based on our research on ontology evolution and ontology-based annotation of documents. The proposed method is implemented in our semantic annotation framework so that efficient acquiring and maintaining of ontology-based metadata is supported
Representational Capacity of Deep Neural Networks -- A Computing Study
There is some theoretical evidence that deep neural networks with multiple
hidden layers have a potential for more efficient representation of
multidimensional mappings than shallow networks with a single hidden layer. The
question is whether it is possible to exploit this theoretical advantage for
finding such representations with help of numerical training methods. Tests
using prototypical problems with a known mean square minimum did not confirm
this hypothesis. Minima found with the help of deep networks have always been
worse than those found using shallow networks. This does not directly
contradict the theoretical findings---it is possible that the superior
representational capacity of deep networks is genuine while finding the mean
square minimum of such deep networks is a substantially harder problem than
with shallow ones
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