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[CfP] Workshop on Scientific Knowledge Graphs (SKG2020) - Deadline approaching - 2 Weeks

From: Angelo Salatino <aas88ie@gmail.com>
Date: Thu, 16 Apr 2020 09:53:14 +0100
Message-ID: <CAOmG_3Xvn8SfYwsmpgZ0kmWrkp5A=sz=y=aagEM3KOu+regTGA@mail.gmail.com>
To: LIS-BIBLIOMETRICS-REQUEST@jiscmail.ac.uk, sigir@acm.org, diglib@iflalists.org, semantic-web@w3.org, AIAI@listserv.heanet.ie, sais-members@lists.lysator.liu.se, issi-society@googlegroups.com, aixia@aixia.it, DIGITAL-LIBRARY-FORUM@jiscmail.ac.uk
Dear all,

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SKG2020
1st Workshop on Scientific Knowledge Graphs
Held in conjunction with TPDL2020 (Lyon, France), 25th August 2020
Twitter: @skgworkshop
Website: https://skg.kmi.open.ac.uk
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Apologies for cross-posting.

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IMPORTANT DATES
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- Paper deadline: ************April 30, 2020********2 weeks
- Notification: May 22, 2020
- Camera-ready due: June 5, 2020
- Workshop day: August 25, 2020

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SCOPE
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In the last decade, we experienced an urgent need for a flexible, context-
sensitive, fine-grained, and machine-actionable representation of scholarly
knowledge and corresponding infrastructures for knowledge curation,
publishing and processing. Such technical infrastructures are becoming
increasingly popular in representing scholarly knowledge as structured,
interlinked, and semantically rich Scientific Knowledge Graphs (SKG).
Knowledge graphs are large networks of entities and relationships, usually
expressed in W3C standards such as OWL and RDF. SKGs focus on the scholarly
domain and describe the actors (e.g., authors, organizations), the documents
(e.g., publications, patents), and the research knowledge (e.g., research
topics, tasks, technologies) in this space as well as their reciprocal
relationships.

Current challenges in this area include: i) the design of ontologies able to
conceptualise scholarly knowledge, ii) (semi-)automatic extraction of
entities and concepts, integration of information from heterogeneous sources,
identification of duplicates, finding connections between entities, and iii)
the development of new services using this data, that allow to explore this
information, measure research impact and accelerate science.

This workshop aims at bringing together researchers and practitioners from
different fields (including, but not limited to, Digital Libraries,
Information Extraction, Machine Learning, Semantic Web, Knowledge
Engineering, Natural Language Processing, Scholarly Communication, and
Bibliometrics) in order to explore innovative solutions and ideas for the
production and consumption of Scientific Knowledge Graphs (SKGs).


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TOPICS
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We encourage the submission of papers covering, but not limited to, one or
more of the following topics:
- Methods for extracting entities (methods, research topics, technologies,
    tasks, materials, metrics, research contributions) and relationships from
    research publications
- Methods for extracting metadata about authors, documents, datasets, grants,
    affiliations and others.
- Data models (e.g., ontologies, vocabularies, schemas) for the description
    of scholarly data and the linking between scholarly data/software and
    academic papers that report or cite them
- Description of citations for scholarly articles, data and software and
    their interrelationships
- Applications for the (semi-)automatic annotation of scholarly papers
- Theoretical models describing the rhetorical and argumentative structure
    of scholarly papers and their application in practice
- Methods for quality assessment of scientific knowledge graphs
- Description and use of provenance information of scholarly data
- Methods for the exploration, retrieval and visualization of scientific
    knowledge graphs
- Pattern discovery of scholarly data
- Scientific claims identification from textual contents
- Automatic or semi-automatic approaches to making sense of research dynamics
- Content- and data-based analysis on scholarly papers
- Automatic semantic enhancement of existing scholarly libraries and papers
- Reconstruction, forecasting and monitoring of scholarly data
- Novel user interfaces for interaction with paper, metadata, content,
    software and data
- Visualisation of related papers or data according to multiple dimensions
    (semantic similarity of abstracts, keywords, etc.)
- Applications for making sense of scholarly data


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SUBMISSION DETAILS
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Submissions are welcome in the following categories:
- Full papers presenting original work (12 pages incl. refer., LNCS format)
- Short papers presenting original work (6 pages incl. refer., LNCS format)

Papers can be submitted via EasyChair:
https://easychair.org/conferences/?conf=skg2020
Submissions will be evaluated based on originality, significance, technical
soundness and clarity.

Accepted papers (after blind review of at least 3 experts) will be published
in the Springer CCIS series. The best papers (according to the reviewers’ rate)
will be invited to a special issue of the **Quantitative Science Studies**
which is an open access journal covering all subject areas related to SKG.

At least one of the authors of the accepted papers must register for the
workshop to be included in the workshop proceedings.

All paper submissions have to be in English and submitted as a PDF file.
Authors should consult Springer’s authors’ guidelines and use their
proceedings templates, either for LaTeX or Word, for the preparation of their
papers. Springer encourages authors to include their ORCIDs in their papers.


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CHAIRS
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Andrea Mannocci, Italian Research Council (CNR), Pisa (IT)
Francesco Osborne, The Open University, Milton Keynes (UK)
Angelo Salatino, The Open University, Milton Keynes (UK)

More information about SKG2020 is available at https://skg.kmi.open.ac.uk
Contact: skg2020@easychair.org
Received on Thursday, 16 April 2020 08:53:39 UTC

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