31.808, Calls: Comp Ling, Semantics/France

Wed Feb 26 16:06:29 UTC 2020

LINGUIST List: Vol-31-808. Wed Feb 26 2020. ISSN: 1069 - 4875.

Subject: 31.808, Calls: Comp Ling, Semantics/France

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Date: Wed, 26 Feb 2020 11:05:44
From: Angelo Salatino [aas88ie at gmail.com]
Subject: 1st Workshop on Scientific Knowledge Graphs

Full Title: 1st Workshop on Scientific Knowledge Graphs 
Short Title: SKG2020 

Date: 25-Aug-2020 - 25-Aug-2020
Location: Lyon, France 
Contact Person: Angelo Salatino
Meeting Email: skg2020 at easychair.org
Web Site: https://skg.kmi.open.ac.uk 

Linguistic Field(s): Computational Linguistics; Semantics 

Call Deadline: 04-Apr-2020 

Meeting Description:

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. These resources provide substantial benefits to researchers,
companies, and policymakers by powering several data-driven services for
navigating, analysing, and making sense of research dynamics. Some examples
include Microsoft Academic Graph (MAG), Open Academic Graph (combining MAG and
AMiner), ScholarlyData, PID Graph, Open Research Knowledge Graph,
OpenCitations, and OpenAIRE research graph. 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).

Call for Papers: 

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

Submission Details: 
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 paper (according to the reviewers’ rate)
will be invited to a special issue of the journal Computer Science and
Information Systems.

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.

Chairs: 
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 at easychair.org

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LINGUIST List: Vol-31-808	
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