Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences
arXiv:2204.04888v2 Announce Type: replace-cross Abstract: In recent years, with the continuous progress of science and technology, the number of scientific research achievements has increased rapidly. As an exchange platform and medium for scientific research achievements, scientific and technological academic conferences have become increasingly abundant. The convening of academic conferences brings large numbers of papers, researchers, institutions, projects, and research topics, but massive conference data also makes it difficult for researchers to obtain valuable information efficiently. I
The rapid increase in scientific publications and academic conferences necessitates more efficient data management and knowledge extraction solutions, making this research timely.
This work directly addresses the challenge of making massive academic data actionable, which is crucial for accelerating research, fostering collaboration, and identifying emergent trends.
The ability to construct accurate knowledge graphs and researcher profiles from conference data will significantly improve the discoverability and utility of scientific information, making it easier for researchers to navigate the academic landscape.
- · Academic researchers
- · Research institutions
- · Conference organizers
- · AI/ML companies specializing in knowledge graphs
- · Inefficient manual data curation processes
- · Researchers struggling with information overload
Improved efficiency in academic information discovery and collaboration.
Faster identification of emerging scientific trends and potential interdisciplinary research areas.
Enhanced ability for funding bodies and policymakers to direct resources strategically based on real-time academic landscape analysis.
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