Summary of Hierarchical Blockmodelling For Knowledge Graphs, by Marcin Pietrasik and Marek Reformat and Anna Wilbik
Hierarchical Blockmodelling for Knowledge Graphs
by Marcin Pietrasik, Marek Reformat, Anna Wilbik
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper investigates the use of probabilistic graphical models, specifically stochastic blockmodels, for hierarchical entity clustering on knowledge graphs. The authors propose a novel model that integrates the Nested Chinese Restaurant Process and the Stick Breaking Process into a generative model, allowing for the induction of hierarchical clusterings without prior constraints. They derive a collapsed Gibbs sampling scheme for inference and evaluate their model on synthetic and real-world datasets, comparing it to benchmark models. The results show that the model is capable of inducing coherent cluster hierarchies in small-scale settings. This work provides a foundation for further applications of stochastic blockmodels for knowledge graphs at larger scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special math models to group similar things together on big databases called knowledge graphs. It’s like finding groups of friends who have something in common, but instead of friends, it’s websites and pieces of information. The authors came up with a new way to do this that works well for small sets of data. They tested their method on real and fake data and compared it to other methods. Their results show that their method can find good groupings, which is important for understanding how the data is connected. |
Keywords
» Artificial intelligence » Clustering » Generative model » Inference
