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However, they also suffer from several shortcomings: closely coupled with entity-linking, rely on table context, need to profile the knowledge graph, and require manual training of the model. Above all, however, they all treat different types of numeric values evenly. In this paper, we tackle these problems and validate our hypothesis: whether taking into account the typology of numeric data in semantic labeling yields better results.<\/jats:p>","DOI":"10.3233\/sw-200397","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T16:31:50Z","timestamp":1601397110000},"page":"5-20","source":"Crossref","is-referenced-by-count":3,"title":["Typology-based semantic labeling of numeric tabular data"],"prefix":"10.1177","volume":"12","author":[{"given":"Ahmad","family":"Alobaid","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Universidad Polit\u00e9cnica de Madrid, 28223, Madrid, Spain. 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