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SKG 2020 : Workshop at TPDL 2020 on SCIENTIFIC KNOWLEDGE GRAPHS

Lyon, France
Event Date: Aug 25, 2020 - Aug 28, 2020
Submission Deadline: Apr 04, 2020
Notification of Acceptance: May 05, 2020
Camera Ready Version Due : Jun 05, 2020

About

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 Paper

We encourage papers that directly contribute to the advancement of the Scientific Knowledge Graphs. Topics include, but are not limited to:

  • 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 research papers (12 pages max)
  • Short research papers (6 pages max)

Papers must comply with the LNCS style and should be submitted in PDF format via the workshop’s EasyChair (skg2020) submission pages.

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 which is an open access journal covering all subject areas related to SKG.

IMPORTANT DATES

  • Paper submission: April 4, 2020
  • Notification of acceptance: May 5, 2020
  • Camera-ready due: June 5, 2020
  • Workshop day: TBA (25th-28th August)

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