The Industry Track at ISWC 2023 covers all aspects of innovative commercial or industrial-strength semantic technologies and Knowledge Graphs in order to showcase the state of adoption.
Knowledge Graphs and semantic technologies are being used in various industries, including automotive, manufacturing, retail, e-commerce, media, finance, telecommunications, healthcare, life sciences, energy, government, intelligence, smart cities, cultural heritage among many others. They enable applications capabilities as varied as business intelligence, analytics, search, content management, knowledge management, recommendation systems, information extraction, master data management, data integration and more.
The Industry Track welcomes contributions about case studies of success stories, as well as discussion reports of obstacles that stand in the way of large-scale adoption of knowledge graph and semantic technologies. Experience reports in applying recent research advances to relevant industry problems are also welcome.
Across all submissions, emphasis should be put on demonstrating the business value and impact created by using knowledge graphs and semantic technologies to address industry real-world problems.
We encourage results and ideas from companies small and large.
Each submission must include at least one author with a non-academic affiliation.
Come and join us at the ISWC 2023 Industry Track to share your experiences with the largest community of researchers and practitioners in the Knowledge Graph and Semantic Web domain!
Important Dates – All deadlines are 23:59 AoE (anywhere on Earth)
|Submissions Due||26th July 2023|
|Notifications Due||25th August 2023|
|Camera-Ready Papers Due||7th September 2023|
Topics of Interest
Topics of interest include, but are not limited to, the following:
- Case studies sharing successful experiences of applying Knowledge Graph and semantic technology to solve a business problem in specific industrial domains
- Discussion reports about obstacles that stand in the way of large-scale adoption of knowledge graph and semantic technologies and proposals to overcome the obstacles
- Experience reports in applying recent research advances of Knowledge Graph and semantic technology to relevant industry problems.
Successful submissions to this track will emphasise:
- Concrete industry/business problem
- Why traditional solutions were not sufficient
- A clear description of the motivation for the need of Knowledge graph and semantic technologies, and the impact in the respective industry
- How knowledge graph technology was applied
- How roadblocks were overcome
- Quantitative measurements of the business value (ROI)
- Discussion of scalability of the presented technologies
- Best practises and lessons learned
- Areas where further research (or research in novel directions) is required, positioning and outlining potential strategic applications and use cases of Knowledge Graph and semantic technologies
The submissions will be reviewed based on the following criteria:
- Quantitative and/or qualitative value proposition provided
- Discussion of innovative aspects, experiences, impact, lessons learned and business value in the application domain.
- Degree to which Knowledge Graph and semantic technologies are critical to the offering
We invite submissions as a 2-page extended abstract formatted in the style of the CEURART style.
Each abstract should describe the content of the proposed talk, which should be of a technical or strategic nature. Pure marketing/promotional material will not be accepted.
Submissions should be made via EasyChair and will be reviewed by a committee of practitioners from industry and academia. At least one author of each accepted paper must register for the conference and present the paper.
Accepted extended abstracts will be distributed to conference attendees and published with CEUR-WS.org.
Accepted extended abstracts will have a presentation at the main conference.
Industry Track Chairs
Jose Manuel Gomez-Perez
Expert AI, Spain
Dr. Daniel Garijo
Artificial Intelligence Department of the Computer Science Faculty of Universidad Politécnica de Madrid