[DL] [CfP] Special Issue on Knowledge Graphs in Semantic Web Journal

Mayank Kejriwal kejriwal at isi.edu
Mon Apr 30 06:30:55 CEST 2018

[Apologies for cross posting; please circulate among your colleagues]

Call for Papers (Semantic Web Journal): Special Issue on Knowledge Graphs:
Construction, Management and QueryingURL: http://www.semantic-web-

A Knowledge Graph (KG) is a graph-theoretic knowledge representation that
(at its simplest) models entities and attribute values as nodes, and
relationships and attributes as labeled, directed edges. Knowledge Graphs
have emerged as a unifying technology in several areas of AI, including
Natural Language Processing and Semantic Web, and for this reason, the
scope of what constitutes a KG has continued to broaden. In industry,
widespread adoption of schema.org, as well as the Google Knowledge Graph,
is changing the way information is being produced and consumed by both
humans and machine agents on the Web. Even before the term ‘Knowledge
Graph’ was coined and was in use, the Semantic Web community was a strong
advocate of many of the core elements that make KGs so powerful, including
graph-theoretic data models (and more generally, semi-structured
representations of both data and schema), powerful pattern-matching
querying languages, graph data management and the emergence and utilization
of large publicly available KGs like DBpedia, GeoNames and Wikidata for
such varied tasks as knowledge acquisition, information retrieval and
knowledge alignment. With the renaissance of, and deep interest in, such
technologies in the broader computer science community, we believe that the
time is ripe for the Semantic Web to revisit Knowledge Graphs from the lens
of construction, management and querying.

We welcome four main types of submissions: (i) full research papers, (ii)
reports on tools and systems, (iii) application reports, and (iv) survey
articles. The description of the submission types is posted at
http://www.semantic-web-journal.net/authors#types. While there is no upper
limit, paper length must be justified by content. For guidance, we provide
a list of possible topics below. Note that these topics are non-exhaustive
and are not meant to be mutually exclusive. We especially welcome
interdisciplinary research that spans multiple topics. Our guest editorial
board includes members from both academia and industry.

Knowledge Graph Construction:

   - Novel techniques and algorithms for information extraction, especially
   algorithms that adapt quickly to novel domains and can be applied to Web
   - Modeling structured sources in terms of a target KG ontology
   - Instance-based or hybrid ontology mapping between the ontologies of
   two KGs
   - Techniques for constructing multi-modal Knowledge Graphs from
   non-textual sources like video, images and other multimedia
   - Crowdsourced techniques for constructing high-quality Knowledge Graphs
   - Interactive techniques such as active learning, question answering and
   dialogs for rapid, high-quality human-in-the-loop KG construction
   - Entity resolution techniques for Knowledge Graphs
   - Machine Learning (including Probabilistic Logic) techniques for
   ‘completing’ Knowledge Graphs by reasoning and doing link prediction over
   information extraction, entity resolution or ontology mapping outputs

Knowledge Graph Querying:

   - Domain-specific search models over Knowledge Graphs, including for
   specialized applications like vertical search and enterprise search
   - Information Retrieval models (including learning to rank models) for
   querying Knowledge Graphs
   - Semantic query reformulation techniques to robustly query noisily
   constructed, or incomplete, Knowledge Graphs
   - Question Answering over Knowledge Graphs Knowledge Graph Management:
   - Entity alignment and linking between diverse Knowledge Graphs
   - Publishing, consumption, maintenance and evolution
   - Personalised learning based on Knowledge Graphs
   - Managing real time and historical data using Knowledge Graphs
   - Security and privacy issues surrounding Knowledge Graph use and


   - Applications that showcase the successful adoption of Knowledge Graphs
   in both research and industrial settings, with clear description of the
   role, impact and motivations behind using Knowledge Graphs.
   - Development and utilization of Knowledge Graphs in specific industrial
   domains (e.g., media, government, financials, healthcare, life sciences,
   smart cities, cultural heritage, etc.) or as a horizontal technology,
   across application areas (e.g., business intelligence, analytics, search,
   content / knowledge management, information extraction, data integration,
   recommendation systems, etc.).
   - Discussion of experiences, scalability and the measurable impact
   (quantitative and / or qualitative) of the added value created by using
   Knowledge Graphs in the respective domain. Best practises and concrete
   lessons learned from these experiences.
   - Potential strategic applications, use cases and areas where further
   research and advances based on using Knowledge Graphs is required


   - Submission deadline: 15 June 2018. Papers submitted before the
   deadline will be reviewed upon receipt.

Submission Instructions

Submissions shall be made through the Semantic Web journal website at
http://www.semantic-web-journal.net. Prospective authors must take notice
of the submission guidelines posted at http://www.semantic-web-

Note that you need to request an account on the website for submitting a
paper. When submitting, please indicate in the cover letter that it is for
the Special Issue on Knowledge Graphs and the chosen submission type. All
manuscripts will be reviewed based on the SWJ open and transparent review
policy and will be made available online during the review process.
Guest editors

The guest editors can be reached at swj-knowledge-graphs at googlegroups.com.

Mayank Kejriwal <http://usc-isi-i2.github.io/kejriwal/> (USC Information
Sciences Institute; Los Angeles, CA, United States)
Vanessa Lopez
<https://researcher.watson.ibm.com/researcher/view.php?person=ie-VANLOPEZ> (IBM
Research; Dublin, Ireland)
Juan F. Sequeda <http://juansequeda.com/> (Capsenta; Austin, TX, United
Guest editorial board

Elena Cabrio, University of Nice Sophia Antipolis, France
Mari Carmen Suarez Figueroa, Universidad Politécnica de Madrid, Spain
Stamatia Dasiopoulou, Pompeu Fabra University, Spain
Dennis Diefenbach, St Etienne university, France
Derek Doran, Wright State University, United States
Mauro Dragoni, Fondazione Bruno Kessler, Italy
Sumit Bhatia, IBM Research, India
Jorge Gracia Del Río, Universidad Politécnica de Madrid, Spain
Dagmar Gromann, Technical University Dresden, Germany
Aidan Hogan, Universidad de Chile, Chile
Freddy Lecue, Accenture Technology Labs, Ireland
Antonio Lieto, University of Turin, Italy
Alessandra Mileo, INSIGHT Center for Data Analytics , Ireland
Andriy Nikolov, metaphacts GmbH, Germany
Sergio Oramas, Pompeu Fabra University, Spain
Petya Osenova, Bulgarian Academy of Sciences, Bulgaria
Raul Palma, Poznan Supercomputing and Networking Center, Poland
Simone Paolo Ponzetto, University of Mannheim, Germany
Hector Perez-Urbina, Google, United States
Silvio Peroni, University of Bologna, Italy
Mariano Rodriguez Muro, IBM Research, United States
Enrico Santus, Singapore University of Technology and Design, Singapore
Kiril Simov, Bulgarian Academy of Sciences, Bulgaria
Michael Spranger, Sony Computer Science Laboratories, Japan
Marta Sabou, Modul University, Austria
Piek Vossen, VU University Amsterdam, The Netherlands
Arkaitz Zubiaga, University of Warwick, United Kingdom
Balaji Ganesa, IBM Research, India
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