Awaad Alsarkhi - Academia.edu (original) (raw)
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Papers by Awaad Alsarkhi
International Journal of Advanced Computer Science and Applications
Deterministic and probabilistic are two approaches to matching commonly used in Entity Resolution... more Deterministic and probabilistic are two approaches to matching commonly used in Entity Resolution (ER) systems. While many users are familiar with writing and using Boolean rules for deterministic matching, fewer are as familiar with the scoring rule configuration used to support probabilistic matching. This paper describes a method using deterministic matching to "bootstrap" probabilistic matching. It also examines the effectiveness three commonly used strategies to mitigate the effect of missing values when using probabilistic matching. The results based on experiment using different sets of synthetically generated data processed using the OYSTER open source entity resolution system.
Journal of Information Technology Management, 2019
This paper discusses recent research on methods for estimating configuration parameters for the M... more This paper discusses recent research on methods for estimating configuration parameters for the Matrix Comparator used for linking unstandardized or heterogeneously standardized references. The matrix comparator computes the aggregate similarity between the tokens (words) in a pair of references. The two most critical parameters for the matrix comparator for obtaining the best linking results are the value of the similarity threshold and the list of stop words to exclude from the comparison. Earlier research has shown that the standard deviation of the token frequency distribution is strongly predictive of how useful stop words will be in improving linking performance. The research results presented here demonstrate a method for using statistics from token frequency distribution to estimate the threshold value and stop word selection likely to give the best linking results. The model was made using linear regression and validated with independent datasets.
International Journal of Advanced Computer Science and Applications
Deterministic and probabilistic are two approaches to matching commonly used in Entity Resolution... more Deterministic and probabilistic are two approaches to matching commonly used in Entity Resolution (ER) systems. While many users are familiar with writing and using Boolean rules for deterministic matching, fewer are as familiar with the scoring rule configuration used to support probabilistic matching. This paper describes a method using deterministic matching to "bootstrap" probabilistic matching. It also examines the effectiveness three commonly used strategies to mitigate the effect of missing values when using probabilistic matching. The results based on experiment using different sets of synthetically generated data processed using the OYSTER open source entity resolution system.
Journal of Information Technology Management, 2019
This paper discusses recent research on methods for estimating configuration parameters for the M... more This paper discusses recent research on methods for estimating configuration parameters for the Matrix Comparator used for linking unstandardized or heterogeneously standardized references. The matrix comparator computes the aggregate similarity between the tokens (words) in a pair of references. The two most critical parameters for the matrix comparator for obtaining the best linking results are the value of the similarity threshold and the list of stop words to exclude from the comparison. Earlier research has shown that the standard deviation of the token frequency distribution is strongly predictive of how useful stop words will be in improving linking performance. The research results presented here demonstrate a method for using statistics from token frequency distribution to estimate the threshold value and stop word selection likely to give the best linking results. The model was made using linear regression and validated with independent datasets.