In this paper, we study the problem of analyzing the relationship between data given in a tabular format and a target knowledge graph, e.g., Wikidata. It is most important to find the label that indicates the correct meaning in Wikidata where data and values are annotated with each label. It is a very difficult task for a machine to correctly understand or infer its meaning. For this to happen, data must be accurately tagged. Wikidata has a label for each document. In addition, it has the characteristic of being linked to another document through these documents. These connected data can be represented as graphs. In this paper, a method is proposed to create a graph based on related elements and infer the relationship of other elements using advanced Wikidata SPARQL queries. Above all, this approach helps in interpreting clear inference relationships and provides a very suitable approach in an environment where data changes frequently such as an open access database.
|Number of pages||8|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2020|
|Event||2020 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2020 - Virtual, Online|
Duration: 2020 Nov 5 → …
Bibliographical noteFunding Information:
* corresponding author † This work is financially supported by Korea Ministry of Land, Infrastructure and Transport(MOLIT) as 「Innovative Talent Education Program for Smart City」. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons Li-cense Attribution 4.0 International (CC BY 4.0).
All Science Journal Classification (ASJC) codes
- Computer Science(all)