Radar Station Supplementary Material 1/2
<p># Radar Station data file. <br> <br> This repo contains the data necessary for the paper 'Radar Station: Using KG Embeddings for Semantic Table Interpretation and Entity Disambiguation.'<br> <br> The second part of the data is available at https://zenodo.org/record/6522921<br> <br> Its structure of it is as follows:<br> <br> '''<br> ├─readme.md<br> ├─DAGOBAHSL_Scoring<br> │ ├─Limaye_Result<br> │ ├─T2D_Result<br> │ ├─2T_Result<br> │ ├─ShortTable_Result<br> ├─Embeddings<br> │ ├─RotetE<br> │ ├─ComplEx<br> │ ├─TransE<br> │ ├─Dismult<br> ├─Datasets<br> │ ├─Key_Column_Index<br> │ │ ├─Limaye<br> │ │ ├─T2D<br> │ ├─ShortTable<br> ├─Wikidata_Ground_Truth<br> │ ├─Wikidata_GS_Limaye<br> │ ├─Wikidata_GS_T2D<br> │ ├─Wikidata_GS_2T<br> │ ├─Wikidata_GS_ShortTable<br> '''<br> <br> ## DAGOBAHSL_Scoring<br> It contains the result of the candidate scoring step after the four datasets were processed through a previous annotation system.<br> In this score step, we did not filter any candidates during the calculation and always kept their scores. <br> <br> <br> ## Embeddings<br> <br> This folder contains four embeddings used during our experiment.<br> We provide four embeddings for the experiments: TransE, RotatE, Dismult, and ComplEx. <br> In which the RotatE embeddings are from the pre-trained embeddings of GraphVite: https://graphvite.io/docs/latest/pretrained_model.html <br> TransE, Dismult, and ComplEx are trained using Pytorch-BigGraph with Wikidata dump version 2021 May.<br> <br> **NOTICE**: the Dismult and ComplEx Embeddings should be further added to this folder.<br> <br> <br> ## Datasets<br> ### Key_Column_Index<br> <br> Key_Column_Index folder contains the index of the key column position for T2D and Limaye that are manually annotated.<br> It tells Radar Station in which column we should run.<br> <br> ### ShortTable<br> <br> It contains the ShortTable dataset with tables of only two rows.<br> <br> ## Wikidata_Ground_Truth<br> It contains the ground truth of the four datasets with Wikidata entities.</p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0
Dataset details
- Repository
- zenodo
- Published
- May 5, 2022
- License
- cc-by-4.0
- DOI
- 10.5281/zenodo.6522984
Scored with release 0.9.0
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