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- W2405940811 abstract "Over the last years, manifold applications that consume, link and integrate Linked Data have been developed. Yet, the specification of integration processes for Linked Data is rendered increasingly tedious by several factors such as the great number of knowledge bases in the Linked Data Cloud as well as schema mismatches and heterogeneous conventions for property values across knowledge bases. Especially the specification of rules for transforming property values has been carried out mostly manually so far. In this paper, we present CaRLA, an algorithm that allows learning transformation rules for pairs of property values expressed as strings. We present both a batch and an active learning version of CaRLA. The batch version of CaRLA uses a three-step learning approach to retrieve probable transformation rules. The active learning version extends the batch version by requesting highly informative property value pairs from the user so as to improve the learning speed of the system. We evaluate both versions of our approach on four experiments with respect to runtime and accuracy. Our results show that we can improve the precision of the data integration process by up to 12% by discovering transformation rules with human accuracy even when provided with small training datasets. In addition, we can even discover rules that were missed by human experts." @default.
- W2405940811 created "2016-06-24" @default.
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- W2405940811 date "2012-11-11" @default.
- W2405940811 modified "2023-09-26" @default.
- W2405940811 title "Learning conformation rules for linked data integration" @default.
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