Browsing University of Waterloo by Supervisor "Ilyas, Ihab"
Now showing items 1-8 of 8
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Entity Matching and Disambiguation Across Multiple Knowledge Graphs
(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
(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
(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 ... -
Private Data Exploring, Sampling, and Profiling
(University of Waterloo, 2022-06-27)Data analytics is being widely used not only as a business tool, which empowers organizations to drive efficiencies, glean deeper operational insights and identify new opportunities, but also for the greater good of society, ... -
Scalability aspects of data cleaning
(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
(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
(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 ... -
Structured Prediction on Dirty Datasets
(University of Waterloo, 2021-12-20)Many errors cannot be detected or repaired without taking into account the underlying structure and dependencies in the dataset. One way of modeling the structure of the data is graphical models. Graphical models combine ...