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They all face efficiency problems, and since they all depend on a rich base of linguistic and world knowledge the knowledge acquisition bottleneck.
Aiming to overcome this knowledge acquisition bottleneck, the Open IE approach was introduced in 2007 in conjunction with the TextRunner system [8].
The results of this experiment provide strong evidence that the Disciple approach is a viable solution to the knowledge acquisition bottleneck.
We started from the assumption that the knowledge acquisition bottleneck was due to the problems of experts expressing their expert knowledge.
Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs.
Imagistic modeling is thus a crucial – and not very broadly appreciated – aspect of the long-standing knowledge acquisition bottleneck in AI.
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However, as knowledge processing technology advances and knowledge acquisition bottlenecks are overcome, it might be possible to overcome the heterogeneity and sparseness of EHR and allow the creation of representative case-bases and the organization of knowledge on principles that facilitate similarity based retrieval.
In the past, machine-learning techniques were successfully developed to ease the knowledge-acquisition bottleneck.
Although a wide variety of expert systems has been built, knowledge acquisition remains a development bottleneck [2, 21].
Machine learning based clinical decision support systems can avoid the bottleneck of knowledge acquisition because knowledge is directly learned through the clinical data.
Ontology learning (OL) from texts has been suggested as a technology that helps to reduce the bottleneck of knowledge acquisition in the construction of domain ontologies.
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