Downloads provided by UsageCounts
This repository contains resources developed for the paper: "S. Zhang, E. Meij, K. Balog, and R. Reinanda. Novel Entity Discovery from Web Tables. In: Proceeding of the The Web Conference 2020 (WWW ’20), April 2020". It includes the three test collections for novel entity discovery for Web tables, entity type and mention resolution, as well as the mention-entity and heading-property correspondences for 3M tables. The cited datasets were used in this work. Files to recreate the entity linking experiments: training_el.csv training_el_type.csv training_el_type_wiki.csv training_el_wiki.csv training_schema.csv Files to recreate the table matching experiments: me_corres.csv - textual cells algorithmically linked to Wikipedia entities hp_corres.csv - same but only table headings Files to recreate the entity resolution experiments: ec_golden.csv - 20K unlinked mentions textual cells, manually linked to Wikipedia er_sf_golden.csv - 1K cell values, manually clustered er_type_golden.csv - 1K cell values, manually linked to DBpedia types
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 23 | |
| downloads | 2 |

Views provided by UsageCounts
Downloads provided by UsageCounts