MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

@article{Park2020MultiImportIN,
  title={MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals},
  author={Namyong Park and Andrey Kan and X. Dong and Tong Zhao and C. Faloutsos},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Namyong Park, Andrey Kan, +2 authors C. Faloutsos
  • Published 2020
  • Computer Science, Mathematics
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the… Expand
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