- From: Sarasi Lalithsena <sarasi2010@gmail.com>
- Date: Wed, 5 Apr 2017 14:41:45 -0400
- To: semantic-web@w3.org
- Message-ID: <CAGVRkGn8vg5Jm1G8FRs-iv_svkhSnh6gHhNwDL7u84Vir9xhDg@mail.gmail.com>
[Apologies if you receive this more than once] Call for Papers Special Track on Knowledge Graph Construction and Consumption (KGCC <https://www.iaria.org/conferences2017/filesICIW17/KGCC.pdf>) at ICIW 2017 <https://www.iaria.org/conferences2017/ICIW17.html> in Venice, Italy OVERVIEW ------------------------------------------ Over the past few years, Semantic Web and Artificial Intelligence research community have a great interest in creating and consuming knowledge graphs. This is further accelerated with the adoption of knowledge graphs by industry giants such as Google, Bing, Yahoo and LinkedIn. These knowledge graphs are being used for various applications such as question answering, recommendation, document similarity/relevance, and knowledge discovery covering variety of domains such as medical and healthcare, entertainment, government and education. Despite the wider adoption, creation and usage of knowledge graphs still needs to tackle several challenges. Most of the existing usable knowledge graphs are built with the human involvement and/or with structured data. Currently, we have access to the ever growing unstructured and semi structured data sources coming from different modalities and it is essential to develop techniques that leverage the richness of these data sources in creating knowledge graphs. This special track is focused on discussing the challenges in creating knowledge graphs and also in consuming knowledge graphs for various applications. TOPICS OF INTEREST ------------------------------------------ Topics include, but are not limited to, the following: - Construction of knowledge graphs semi-automatically or automatically from various heterogeneous multi-modal data sources; formal text, short text, images, sensor data, etc - Construct knowledge graphs for focused and specialized domains - Structured machine learning on knowledge graphs - Knowledge graph embedding in vector spaces - Improve the quality of the knowledge graphs by dealing with the noise and incompleteness of existing knowledge graphs - Maintain the temporal relevancy of knowledge graphs; i.e., identify emerging/fading concepts and facts - Integration of knowledge graphs; alignment techniques to align classes, relationships and instances - Applications of leveraging knowledge graphs to improve the state of the art techniques - Scalability challenges in leveraging large knowledge graphs CONTRIBUTION TYPES ------------------------------------------ - Regular papers [in the proceedings, digital library] - Short papers (work in progress) [in the proceedings, digital library] - Posters: two pages [in the proceedings, digital library] - Posters: slide only [slide-deck posted online] - Presentations: slide only [slide-deck posted online] - Demos: two pages [posted online] IMPORTANT DATES ------------------------------------------ Submission deadline: May 17, 2017, Hawaii Time (GMT-10) Contributions are to be submitted to the submission management system <https://www.iariasubmit.org/conferences/submit/newcontribution.php?event=ICIW+2017+Special> . For more details, read the complete Call of Papers <https://www.iaria.org/conferences2017/filesICIW17/KGCC.pdf>. CONTACT ------------------------------------------ Sarasi Lalithsena, Kno.e.sis Center, Wright State University, USA sarasi@knoesis.org Tommaso Soru, AKSW, University of Leipzig, Germany tsoru@informatik.uni-leipzig.de
Received on Wednesday, 5 April 2017 18:42:20 UTC