Abstract
The number of datasets in the Linking Open Data (LOD) cloud as well as LOD-based applications have exploded in the last years. However, because of data source heterogeneity, published data may suffer of redundancy, inconsistencies, or may be incomplete; thus, results generated by LOD-based applications may be imprecise, ambiguous, or unreliable. We demonstrate the capabilities of LiQuate (Linked Data Quality Assessment), a tool that relies on Bayesian Networks to analyze the quality of data and links in the LOD cloud.
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CITATION STYLE
Ruckhaus, E., Vidal, M. E., Castillo, S., Burguillos, O., & Baldizan, O. (2014). Analyzing linked data quality with LiQuate. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8798, 488–493. https://doi.org/10.1007/978-3-319-11955-7_72
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