High Quality Data Generation: An Ontology Reasoning based Approach

Yue MaYue Ma, Julian MendezJulian Mendez

Yue Ma, Julian Mendez
High Quality Data Generation: An Ontology Reasoning based Approach
International Workshop on Artificial Intelligence for Big Data (in conjunction with IJCAI'13), to appear

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KurzfassungAbstract
As Big Data is getting increasingly more helpful for different applications, the problem of obtaining reliable data becomes important. The importance is more obvious for domain specific applications because of their abstruse domain knowledge. Most of the Big Data based techniques manipulate directly datasets under the assumption that data quantity can lead to a good system quality. In this paper, we show that the quality can be improved by automatically enriching a given dataset with more high-quality data beforehand. This is achieved by a tractable reasoning technique over the widely used biomedical ontology SNOMED CT. Our approach is evaluated by the scenario of formal definition generation from natural language texts, where the average precision of learned definitions is improved by 5.3%.
Bemerkung: Note: To appear.
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

@inproceedings{ MaMe-AIBD13,
  address = {Dresden, Germany},
  author = {Yue {Ma} and Julian {Mendez}},
  booktitle = {International Workshop on {A}rtificial {I}ntelligence for {B}ig {D}ata (in conjunction with IJCAI'13)},
  note = {To appear.},
  title = {High Quality Data Generation: An Ontology Reasoning based Approach},
  year = {2013},
}