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LinkingPark: Automatic Semantic Table Interpretation Software

Authors: Shuang Chen; Alperen Karaoglu; Carina Negreanu; Börje F. Karlsson; Tingting Ma; Jin-Ge Yao; Jack Williams; +3 Authors

LinkingPark: Automatic Semantic Table Interpretation Software

Abstract

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.

Related Organizations
Keywords

semantic web, tabular data, semtab, entity linking, nlp, knowledge graph matching

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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).
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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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