posted by user: angelosalatino || 1276 views || tracked by 2 users: [display]

SKG 2020 : 1st​ Workshop on Scientific Knowledge Graphs


When Aug 25, 2020 - Aug 28, 2020
Where Lyon, FR
Submission Deadline Apr 4, 2020
Notification Due May 5, 2020
Final Version Due Jun 5, 2020
Categories    digital libraries   knowledge graphs   science of science   semantic web

Call For Papers

1st​ Workshop on Scientific Knowledge Graphs
Held in conjunction with TPDL2020 (Lyon, France), 25th-28th August 2020
Twitter: @skgworkshop

Apologies for cross-posting.


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


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

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).


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


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:
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.


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

Related Resources

IJCKG 2021   International Joint Conference on Knowledge Graphs
DMA 2022   8th International Conference on Data Mining and Applications
KG@SAC 2022   ACM SAC 2022 Track on Knowledge Graphs
PAKDD 2022   The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022)
JCDL 2022   ACM/IEEE Joint Conference on Digital Libraries
Sci-K 2021   1st International Workshop on Scientific Knowledge Representation, Discovery, and Assessment
ARIMA 2021   Special Issue Data Intelligibility, Business Intelligence and Semantic Web
KSEM 2022   International Conference on Knowledge Science, Engineering and Management
IRCDL 2022   18th Italian Research Conference on Digital Libraries (IRCDL 2022)
AAAI-MAKE 2022   AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence