Summary of Posets and Bounded Probabilities For Discovering Order-inducing Features in Event Knowledge Graphs, by Christoffer Olling Back and Jakob Grue Simonsen
Posets and Bounded Probabilities for Discovering Order-inducing Features in Event Knowledge Graphs
by Christoffer Olling Back, Jakob Grue Simonsen
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper tackles the problem of automating event knowledge graph (EKG) discovery from uncurated data using a probabilistic framing. The approach is principled and based on the outcome space resulting from featured-derived partial orders on events. This leads to an EKG discovery algorithm that uses statistical inference rather than ad-hoc or heuristic-based strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand process executions better by discovering multiple, interacting views of a process through event knowledge graphs. It’s like taking a bunch of puzzle pieces and figuring out how they fit together to show what happened during the process. |
Keywords
» Artificial intelligence » Inference » Knowledge graph
