Comments (6)
Hi @chenwuperth, thank you very much for your interest in our research. After we used the entity mention extraction and linking system to extract important entities from the title, we will use those mentions to find their corresponding head or tail entities in the enhanced Knowledge base which is enhanced by the link prediction result. Those related head or tails and the extracted mentions together formed our terms for each pair of training, valid, or testing data.
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After you extract entities and relationships from the reference topic, how do you find the relevant head or tail nodes in the enhanced knowledge graph? How do you define the relevance? What is the method used?
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So I did the link prediction (which is in the github existing paper reading) over all entities in the knowledge graph and choose the top 10 mentions as the relevant entites
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Then we used all those triples as an enhanced knowledge graph
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Thank you for your prompt reply, I got it!
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Hope you have a great day!
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Related Issues (20)
- excellent completed work !! can you share some details about how to produce the dataset,such as entity_text_title_tokenized.json,term.pth in PubMed Paper Reading Dataset ? thanks HOT 36
- Confusingly slow on testing of existing_model_reading model HOT 5
- 处理one-hop节点的代码的一个疑问 HOT 4
- It said that I need to have more RAM?tks HOT 12
- How many epoches do you use in these two tasks? HOT 3
- Training part of the dataset HOT 2
- --
- Model weights sharing and training stopping criterion HOT 3
- Something has been deprecated. HOT 1
- How to generate KGs? HOT 4
- Questions about code and paper_reading dataset HOT 2
- 这个项目现在还更新吗,还有人在使用吗?
- PubTator-MeSH-CTD HOT 1
- METEOR in eval.py HOT 4
- CUDA out of memory HOT 7
- Are new entities formed or just links? HOT 2
- RuntimeError HOT 13
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 18
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 3
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