16,656 research outputs found
Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles
Purpose. This paper introduces the Research Articles in Simplified HTML (or RASH), which is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the RASH Framework, a set of tools for interacting with RASH-based articles. The paper also presents an evaluation that involved authors and reviewers of RASH articles submitted to the SAVE-SD 2015 and SAVE-SD 2016 workshops.
Design. RASH has been developed aiming to: be easy to learn and use; share scholarly documents (and embedded semantic annotations) through the Web; support its adoption within the existing publishing workflow.
Findings. The evaluation study confirmed that RASH is ready to be adopted in workshops, conferences, and journals and can be quickly learnt by researchers who are familiar with HTML.
Research Limitations. The evaluation study also highlighted some issues in the adoption of RASH, and in general of HTML formats, especially by less technically savvy users. Moreover, additional tools are needed, e.g., for enabling additional conversions from/to existing formats such as OpenXML.
Practical Implications. RASH (and its Framework) is another step towards enabling the definition of formal representations of the meaning of the content of an article, facilitating its automatic discovery, enabling its linking to semantically related articles, providing access to data within the article in actionable form, and allowing integration of data between papers.
Social Implications. RASH addresses the intrinsic needs related to the various users of a scholarly article: researchers (focussing on its content), readers (experiencing new ways for browsing it), citizen scientists (reusing available data formally defined within it through semantic annotations), publishers (using the advantages of new technologies as envisioned by the Semantic Publishing movement).
Value. RASH helps authors to focus on the organisation of their texts, supports them in the task of semantically enriching the content of articles, and leaves all the issues about validation, visualisation, conversion, and semantic data extraction to the various tools developed within its Framework
BlogForever D2.4: Weblog spider prototype and associated methodology
The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype
Enhanced Integrated Scoring for Cleaning Dirty Texts
An increasing number of approaches for ontology engineering from text are
gearing towards the use of online sources such as company intranet and the
World Wide Web. Despite such rise, not much work can be found in aspects of
preprocessing and cleaning dirty texts from online sources. This paper presents
an enhancement of an Integrated Scoring for Spelling error correction,
Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as
part of a text preprocessing phase in an ontology engineering system. New
evaluations performed on the enhanced ISSAC using 700 chat records reveal an
improved accuracy of 98% as compared to 96.5% and 71% based on the use of only
basic ISSAC and of Aspell, respectively.Comment: More information is available at
http://explorer.csse.uwa.edu.au/reference
Research and Development Workstation Environment: the new class of Current Research Information Systems
Against the backdrop of the development of modern technologies in the field
of scientific research the new class of Current Research Information Systems
(CRIS) and related intelligent information technologies has arisen. It was
called - Research and Development Workstation Environment (RDWE) - the
comprehensive problem-oriented information systems for scientific research and
development lifecycle support. The given paper describes design and development
fundamentals of the RDWE class systems. The RDWE class system's generalized
information model is represented in the article as a three-tuple composite web
service that include: a set of atomic web services, each of them can be
designed and developed as a microservice or a desktop application, that allows
them to be used as an independent software separately; a set of functions, the
functional filling-up of the Research and Development Workstation Environment;
a subset of atomic web services that are required to implement function of
composite web service. In accordance with the fundamental information model of
the RDWE class the system for supporting research in the field of ontology
engineering - the automated building of applied ontology in an arbitrary domain
area, scientific and technical creativity - the automated preparation of
application documents for patenting inventions in Ukraine was developed. It was
called - Personal Research Information System. A distinctive feature of such
systems is the possibility of their problematic orientation to various types of
scientific activities by combining on a variety of functional services and
adding new ones within the cloud integrated environment. The main results of
our work are focused on enhancing the effectiveness of the scientist's research
and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian.
Published. Prepared for special issue (UkrPROG 2018 conference) of the
scientific journal "Problems of programming" (Founder: National Academy of
Sciences of Ukraine, Institute of Software Systems of NAS Ukraine
Integrating and querying similar tables from PDF documents using deep learning
Large amount of public data produced by enterprises are in semi-structured
PDF form. Tabular data extraction from reports and other published data in PDF
format is of interest for various data consolidation purposes such as analysing
and aggregating financial reports of a company. Queries into the structured
tabular data in PDF format are normally processed in an unstructured manner
through means like text-match. This is mainly due to that the binary format of
PDF documents is optimized for layout and rendering and do not have great
support for automated parsing of data. Moreover, even the same table type in
PDF files varies in schema, row or column headers, which makes it difficult for
a query plan to cover all relevant tables. This paper proposes a deep learning
based method to enable SQL-like query and analysis of financial tables from
annual reports in PDF format. This is achieved through table type
classification and nearest row search. We demonstrate that using word embedding
trained on Google news for header match clearly outperforms the text-match
based approach in traditional database. We also introduce a practical system
that uses this technology to query and analyse finance tables in PDF documents
from various sources
Textpresso for Neuroscience: Searching the Full Text of Thousands of Neuroscience Research Papers
Textpresso is a text-mining system for scientific literature. Its two major features are access to the full text of research papers and the development and use of categories of biological concepts as well as categories that describe or relate objects. A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. Here we describe Textpresso for
Neuroscience, part of the core Neuroscience Information Framework
(NIF). The Textpresso site currently consists of 67,500 full text
papers and 131,300 abstracts. We show that using categories in
literature can make a pure keyword query more refined and meaningful.
We also show how semantic queries can be formulated with categories
only. We explain the build and content of the database and describe the
main features of the web pages and the advanced search options. We also
give detailed illustrations of the web service developed to provide
programmatic access to Textpresso. This web service is used by the NIF
interface to access Textpresso. The standalone website of Textpresso
for Neuroscience can be accessed at
http://www.textpresso.org/neuroscience
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