Now showing items 1-6 of 6

    • Entity Matching and Disambiguation Across Multiple Knowledge Graphs 

      Farag, Michael (University of Waterloo, 2019-06-10)
      Knowledge graphs are considered an important representation that lie between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a back-bone of many applications on the Web. With ...
    • Extracting and Cleaning RDF Data 

      Farid, Mina (University of Waterloo, 2020-05-28)
      The RDF data model has become a prevalent format to represent heterogeneous data because of its versatility. The capability of dismantling information from its native formats and representing it in triple format offers a ...
    • Knowledge Graph Imputation 

      Niknamian, Mohammadali (University of Waterloo, 2021-05-27)
      Knowledge graphs are one of the most important resources of information in many applications such as question answering and social networks. These knowledge graphs however, are often far from complete as there are so many ...
    • Scalability aspects of data cleaning 

      Saxena, Hemant (University of Waterloo, 2021-01-27)
      Data cleaning has become one of the important pre-processing steps for many data science, data analytics, and machine learning applications. According to a survey by Gartner, more than 25% of the critical data in the world's ...
    • Scalable and Holistic Qualitative Data Cleaning 

      Chu, Xu (University of Waterloo, 2017-08-14)
      Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Poor data across businesses and the government cost the ...
    • Scaling Machine Learning Data Repair Systems for Sparse Datasets 

      Attia, Omar (University of Waterloo, 2021-01-21)
      Machine learning data repair systems (e.g. HoloClean) have achieved state-of-the-art performance for the data repair problem on many datasets. However, these systems face significant challenges with sparse datasets. In ...

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