Summary of Causallp: Learning Causal Relations with Weighted Knowledge Graph Link Prediction, by Utkarshani Jaimini et al.
CausalLP: Learning causal relations with weighted knowledge graph link prediction
by Utkarshani Jaimini, Cory Henson, Amit P. Sheth
First submitted to arxiv on: 23 Apr 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 presents CausalLP, a novel approach to addressing incomplete causal networks in various applications such as medical diagnosis and manufacturing root-cause analysis. It formulates this issue as a knowledge graph completion problem, mapping the task of finding new causal relations to knowledge graph link prediction. This approach enables integration of external domain knowledge and incorporates weighted causal relations representing the strength of associations between entities. The method supports two primary tasks: causal explanation and causal prediction. The performance is evaluated using the CLEVRER-Humans benchmark dataset for causal reasoning, comparing multiple knowledge graph embedding algorithms. Two distinct dataset splitting approaches are used: random-based split and Markov-based split, which utilizes the Markovian property of causal relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how things cause each other in different areas like medicine and manufacturing. Right now, these networks are often incomplete with missing connections. The authors created a new way to fix this problem by using something called knowledge graphs. These graphs help combine information from outside sources to make the network more complete. They also added weights to show how strong the connections are between things. The method can do two important tasks: explain why certain causes happened and predict what will happen in the future. To test it, they used a special dataset with videos that demonstrate causal reasoning. They compared different ways of splitting this data to see which one works best. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
