80,813 research outputs found
Editorial stance on duplicate and salami publication
In this edition of the British Orthoptic Journal the notice
to contributors has been amended. The sentence ‘Papers
are considered for publication on the understanding that
they are not being submitted elsewhere at the same time’
has been extended to address the problem of duplicate
publication and now appears under ‘Terms of submission’
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called "matching dependencies" (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating four
components of ER: (a) Building a classifier for duplicate/non-duplicate record
pairs built using machine learning (ML) techniques; (b) Use of MDs for
supporting the blocking phase of ML; (c) Record merging on the basis of the
classifier results; and (d) The use of the declarative language "LogiQL" -an
extended form of Datalog supported by the "LogicBlox" platform- for all
activities related to data processing, and the specification and enforcement of
MDs.Comment: Final journal version, with some minor technical corrections.
Extended version of arXiv:1508.0601
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called matching dependencies (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating three
components of ER: (a) Classifiers for duplicate/non-duplicate record pairs
built using machine learning (ML) techniques, (b) MDs for supporting both the
blocking phase of ML and the merge itself; and (c) The use of the declarative
language LogiQL -an extended form of Datalog supported by the LogicBlox
platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201
Publications ethics
The editor of any medical journal has to be aware of the ethical and legal framework within which medical research is conducted. When research and publications relate to children, then particularly high standards are required in the design, conduct, and reporting of research in order to protect the rights of children and their families. Authors have a number of duties and responsibilities that are mirrored by those of editors and publishers. Of particular importance are the principles of transparency and integrity. Authors should be explicit about who carried out the work and who funded the study. They should declare whether the work has been published before and is not being considered for publication elsewhere. The authors must protect the rights of research participants including their anonymity. Editors and publishers have a duty to ensure high editorial standards and efficient and effective peer review systems. They should follow ethical and responsible publication practices and should safeguard the intellectual property of the authors. This review discusses in detail the duties and responsibilities of authors, editors, and publishers in modern medical publishing
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