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- W49700711 abstract "AbstractThe various approaches described so far are effective for controlled tasks such as annotating a collection of patient records. In reality, very few of these techniques really scale effectively to produce an ongoing stream of annotations. However, every controlled task is problematic. We live in a dynamic world where things constantly change and probably those annotations would have to change with time. The patient record would have to be updated, patients die and new ones are recorded. So rather than inserting the annotations in the records, the best approach would be to create an ontology and insert in the ontologies a reference to the instances rather than the actual instances. In this way, if the instance changes slightly, there is no need of modifying all the ontologies where this instance appears since the link would still be valid. This also makes sense because in our world and even on the Internet, there exists no Oracle of Delphi [74] that has the answers to all the possible questions thus we can never be sure of the validity of our data. Knowledge is by nature distributed and dynamic, and the most plausible scenario in the future [108] seems to be made up of several distributed ontologies which share concepts between them. This document already delved into the issues why the annotation task might be difficult when performed by humans. If we think about the current size and growth of the web [62], it is already an unmanageable process to manually annotate all of those pages. If we re-dimension our expectations and try to annotate just the newly created documents, it is still a slow time-consuming process that involves high costs. Due to these problems, it is vital to create methodologies that help users during the annotation of these documents in a semiautomatic way.KeywordsInformation ExtractionManual AnnotationAnnotation ToolAnnotation ProcessTemporal ElementThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W49700711 created "2016-06-24" @default.
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- W49700711 date "2011-01-01" @default.
- W49700711 modified "2023-09-26" @default.
- W49700711 title "Semi-automated Annotation" @default.
- W49700711 doi "https://doi.org/10.1007/978-3-642-20323-7_6" @default.
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