Abstract
Linked Open Data has proven useful in disambiguation and query extension tasks, but their incomplete and inconsistent nature may make them less useful in analyzing brief, low-level business transactions. In this paper, we investigate the effect of using Wikidata and DBpedia to aid in classification of real bank transactions. The experiments indicate that Linked Open Data may have the potential to supplement transaction classification systems effectively. However, given the nature of the transaction data used in this research and the current state of Wikidata and DBpedia, the extracted data has in fact a negative impact the accuracy on the classification model when compared to the Baseline approach. The Baseline approach produces an accuracy score of 88,60% where the Wikidata, DBpedia and their combined approaches yield accuracy scores of 84,99%, 86,65% and 83,48%.
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Folkestad, E., Vollset, E., Gallala, M.R., Gulla, J.A. (2017). Why Enriching Business Transactions with Linked Open Data May Be Problematic in Classification Tasks. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_24
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DOI: https://doi.org/10.1007/978-3-319-69548-8_24
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