%0 Conference Paper %1 10.1007/978-3-030-21462-3_21 %A Hanika, Tom %A Marx, Maximilian %A Stumme, Gerd %B Formal Concept Analysis %C Cham %D 2019 %E Cristea, Diana %E Le Ber, Florence %E Sertkaya, Baris %I Springer International Publishing %K citationstyle csl testing %P 315--323 %T Discovering Implicational Knowledge in Wikidata %X Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Among the freely available knowledge graphs, Wikidata stands out by being collaboratively edited and curated. Among the vast numbers of facts, complex knowledge is just waiting to be discovered, but the sheer size of Wikidata makes this infeasible for human editors. We apply Formal Concept Analysis to efficiently identify and succinctly represent comprehensible implications that are implicitly present in the data. As a first step, we describe a systematic process to extract conceptual knowledge from Wikidata's complex data model, thus providing a method for obtaining large real-world data sets for FCA. We conduct experiments that show the principal feasibility of the approach, yet also illuminate some of the limitations, and give examples of interesting knowledge discovered. %@ 978-3-030-21462-3

@inproceedings{10.1007/978-3-030-21462-3_21, abstract = {Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Among the freely available knowledge graphs, Wikidata stands out by being collaboratively edited and curated. Among the vast numbers of facts, complex knowledge is just waiting to be discovered, but the sheer size of Wikidata makes this infeasible for human editors. We apply Formal Concept Analysis to efficiently identify and succinctly represent comprehensible implications that are implicitly present in the data. As a first step, we describe a systematic process to extract conceptual knowledge from Wikidata's complex data model, thus providing a method for obtaining large real-world data sets for FCA. We conduct experiments that show the principal feasibility of the approach, yet also illuminate some of the limitations, and give examples of interesting knowledge discovered.}, added-at = {2019-11-26T12:58:27.000+0100}, address = {Cham}, author = {Hanika, Tom and Marx, Maximilian and Stumme, Gerd}, biburl = {https://www.bibsonomy.org/bibtex/25817a1ec89f180784fd1431304c1f168/citationstyle}, booktitle = {Formal Concept Analysis}, description = {Discovering Implicational Knowledge in Wikidata | SpringerLink}, editor = {Cristea, Diana and Le Ber, Florence and Sertkaya, Baris}, interhash = {88b09220487805d3b99270c0349e7062}, intrahash = {5817a1ec89f180784fd1431304c1f168}, isbn = {978-3-030-21462-3}, keywords = {citationstyle csl testing}, pages = {315--323}, publisher = {Springer International Publishing}, timestamp = {2019-11-26T12:58:27.000+0100}, title = {Discovering Implicational Knowledge in Wikidata}, year = 2019 }