Downloads provided by UsageCounts
LinkingPark is an open-sourced system for automatic semantic table interpretation. Given a table of data, the system can match tabular elements to existing knowledge graphs (in this release, primarily Wikidata). Such semantic information from tabular data can then empower various downstream applications, e.g., intelligent spreadsheet programs, knowledge base population, data integration and cleaning, etc. LinkinkingPark is unsupervised, stand-alone, and also provides flexibility for multilingual support out of the box. This initial public release builds on top of the previous internal version that won 2nd prize in the SemTab 2020 competition and provides much improved efficiency and stability while maintaining or improving correctness over different datasets.
LinkingPark is available on GitHub: https://github.com/microsoft/vert-papers/tree/master/papers/LinkingPark. Contributions and collaboration are welcome.
semantic web, tabular data, semtab, entity linking, nlp, knowledge graph matching
semantic web, tabular data, semtab, entity linking, nlp, knowledge graph matching
| 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 | 11 | |
| downloads | 1 |

Views provided by UsageCounts
Downloads provided by UsageCounts