Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition
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    Abstract:

    Government data governance is undergoing a new phase of transition from "physical data aggregation" to "logical semantic unification". Thus far, long-term "autonomy" of government information silos, lead to a wide spectrum of metadata curation issues, such as attributes with the same names but having different meanings, or attributes with different names but having the same meanings. Instead of either rebuilding/modifying legacy information systems or physically aggregating data from isolated information systems, logical semantic unification solves this problem by unifying the semantic expression of the metadata in government information silos and achieves the standardized metadata governance. This work semantically aligns the metadata of each government information silo to the existing standard metadata. Specifically, the standard metadata names are viewed as semantic labels, and the semantic meanings of columns of relations in each government information silo are semantically identified, so as to establish the semantic alignment of column names and standard metadata and achieve standardized governance of silo metadata.

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高珊,袁宛竹,卢卫,王兰,张静,杜小勇.面向列语义识别的共现属性交互模型构建与优化.软件学报,2023,34(3):1010-1026

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History
  • Received:May 15,2022
  • Revised:July 29,2022
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  • Online: October 26,2022
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