Summary of Veni, Vidi, Vici: Solving the Myriad Of Challenges Before Knowledge Graph Learning, by Jeffrey Sardina et al.
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning
by Jeffrey Sardina, Luca Costabello, Christophe Guéret
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research addresses four key deficiencies in state-of-the-art graph learning systems for Knowledge Graphs (KGs), which are essential for large-scale linked data analysis, interpretation, and pattern detection. The identified shortcomings include the lack of expert knowledge integration, instability to node degree extremity, neglect of uncertainty and relevance, and limited explainability. The study highlights that existing attempts to solve these problems have been isolated from each other, hindering human-KG empowerment. A holistic framework called “Veni, Vidi, Vici” is proposed to address these limitations and promote co-empowerment between humans and KG learning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to improve graph learning systems for Knowledge Graphs by addressing four main issues: not using expert knowledge, being unstable when dealing with certain types of nodes, ignoring uncertainty and relevance, and lacking explanation. These problems are currently solved separately, but the study shows that this approach is holding back progress in human-KG collaboration. The proposed solution, called “Veni, Vidi, Vici”, integrates these solutions to achieve better results. |
