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Description
There are many applications where the truth is unknown. The truth values are
guessed by different sources. The values of different properties can be obtained from
various sources. These will lead to the disagreement in sources. An important task
is to obtain the truth from these sometimes contradictory sources. In the extension
of computing the truth, the reliability of sources needs to be computed. There are
models which compute the precision values. In those earlier models Banerjee et al.
(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and
Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled
individually. In one of the existing works, the heterogeneous properties are modeled in
a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous
Data (CRH) framework is based on the single objective optimization. Due to the
single objective optimization and non-convex optimization problem, only one local
optimal solution is found. As this is a non-convex optimization problem, the optimal
point depends upon the initial point. This single objective optimization problem is
converted into a multi-objective optimization problem. Due to the multi-objective
optimization problem, the Pareto optimal points are computed. In an extension of
that, the single objective optimization problem is solved with numerous initial points.
The above two approaches are used for finding the solution better than the solution
obtained in the CRH with median as the initial point for the continuous variables and
majority voting as the initial point for the categorical variables. In the experiments,
the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective
optimization and the solution coming from the CRH is the optimum solution
in these experiments.
guessed by different sources. The values of different properties can be obtained from
various sources. These will lead to the disagreement in sources. An important task
is to obtain the truth from these sometimes contradictory sources. In the extension
of computing the truth, the reliability of sources needs to be computed. There are
models which compute the precision values. In those earlier models Banerjee et al.
(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and
Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled
individually. In one of the existing works, the heterogeneous properties are modeled in
a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous
Data (CRH) framework is based on the single objective optimization. Due to the
single objective optimization and non-convex optimization problem, only one local
optimal solution is found. As this is a non-convex optimization problem, the optimal
point depends upon the initial point. This single objective optimization problem is
converted into a multi-objective optimization problem. Due to the multi-objective
optimization problem, the Pareto optimal points are computed. In an extension of
that, the single objective optimization problem is solved with numerous initial points.
The above two approaches are used for finding the solution better than the solution
obtained in the CRH with median as the initial point for the continuous variables and
majority voting as the initial point for the categorical variables. In the experiments,
the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective
optimization and the solution coming from the CRH is the optimum solution
in these experiments.
ContributorsJain, Karan (Author) / Xue, Guoliang (Thesis advisor) / Sen, Arunabha (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2019