Typology-based semantic labeling of numeric tabular data
| Autor: | Ahmad Alobaid, Oscar Corcho, Emilia Kacprzak |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Typology
Computer Networks and Communications business.industry Computer science 02 engineering and technology computer.software_genre Computer Science Applications Semantic labeling 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Information Systems |
| Zdroj: | Semantic Web. 12:5-20 |
| ISSN: | 2210-4968 1570-0844 |
| DOI: | 10.3233/sw-200397 |
| Popis: | A lot of tabular data are being published on the Web. Semantic labeling of such data may help in their understanding and exploitation. However, many challenges need to be addressed to do this automatically. With numbers, it can be even harder due to the possible difference in measurement accuracy, rounding errors, and even the frequency of their appearance. Multiple approaches have been proposed in the literature to tackle the problem of semantic labeling of numeric values in existing tabular datasets. 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. |
| Databáze: | OpenAIRE |
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