Exploiting Context Analysis for Combining Multiple Entity Resolution Systems -Ramu Bandaru Zhaoqi Chen Dmitri V.kalashnikov Sharad Mehrotra

Table of Contents Introduction Related Work Problem Definition Context based Framework Experimental Evaluation Conclusion

Introduction This paper proposes a new Entity Resolution Ensemble framework. The real world raw datasets are often not perfect and contain various types of issues, including errors and uncertainty, which complicate analysis. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The goal of ER is to identify and group references that co-refer. The output of an ER system is a clustering of references, where each cluster is supposed to represent one distinct entity.

There are diverse ER approaches have been proposed to address the entity resolution challenge. This motivates the problem of creating a single combined ER solution – an ER ensemble. Different ER solutions perform better in different contexts. No solution is the best in terms of Quality. ER ensemble actively uses the notion of the correct clustering A + for D. Each system S i is applied to the dataset being processed to produce its clustering A (i). Clustering A (i) corresponds to what system S i believes A + should be.

The goal of ER ensemble is to build a clustering A * with the highest quality which is as close to A + as possible. Difference between the two ensemble problems: The objective of the cluster ensemble will be to find a clustering that agrees the most with the n clusterings, including the n−1 poor-quality clusters. The goal of the ER ensemble is to simply output A (n) as its A *,and completely ignore the rest of the clusterings. ▫Let K be the true number of clusters if (K is large) then high threshold if (K is small) then low threshold

Two novel ER ensemble approaches. A predictive way of selecting context features that relies on estimating the unknown parameters of the data being processed. An extensive empirical evaluation of the proposed solution.

Related Work Entity Resolution : Many of the approaches have focused on exploiting various similarity metrics, relational similarities, probabilistic models. Clustering Ensembles : The final clustering is usually the one that agrees the most with the given clusterings. Combining Classifiers: Majority Voting-popular rule Bagging & Boosting-combine classifiers of same type. Stacking is to perform cross-validation on the base-level dataset and create a meta-level dataset from the predictions of base-level classifiers.

Problem Definition Entity Resolution Two references r i and r j are said to co-refer, denoted as r i ~ r j, if they refer to the same object, that is, if o ri = o rj. The co-reference set C r for r is the set of all references that co-refer with r ie., C r = {q belongs to R:q~r}. The output of an ER system S applied to dataset D is a set of clusters A = {A 1,A 2,...,A |A| }. To assess the quality of the resulting clusterings, we evaluate how different the clustering A is from the ground truth A +.

ER Ensemble These systems can be applied to dataset D to cluster the set of references R. Each system S i will produce its set of clusters A (i) = {A 1 (i),A (i) 2,...,A (i) |Ai| }. The goal is to be able to combine the resulting clusterings A (1),A (2),...,A (n) into a single clustering A * such that the quality of A * is higher than that of A (i), for i = 1, 2,..., n. The task of ER ensemble is for each e j belongs to E to provide a mapping d j ->a * j. Here, a * j belongs {−1, 1} is the prediction of the combined algorithm for edge e j, where a * j = 1 if the overall algorithm believes a + j = 1, and a * j =−1 otherwise.

Problem Definition Using Blocking to Improve Efficiency : Virtual Connected Subgraph(VCS). Blocking is important to improve the efficiency and also the quality of the algorithms. Naïve Solution: Voting – It will count predictions. Advanced Voting Scheme is Weighted Voting(WV). Problem with voting is that it is a static non-adaptive scheme with respect to the context.

Context Based Framework Context Features : Effectiveness and Generality Number of Clusters: This is derived from the expected number of clusters per VCS as well as the number of clusters generated by each base-level system. Node Fanout: It exploits the nodes adjacent to the edges at the edge level. The overall context feature vector fj is computed as a linear combination of the feature vectors For m features, Adding more context features can increase the robustness and accuracy of the system

Overview of the Approach

Meta-level Classification 2 algorithms for meta-level classification: Context-Extended Classification: Uses Regression to find K * and N v *.The computes f ji. Now it is possible to learn a mapping from features d j ×f j into edge predictions a j * by training a classifier using the ground truth labels a j +.

Context-Weighted Classification: The idea of this approach is to learn for each base-level system S i a model M i that would predict how accurate S i tends to be in a given context.

Creating Final Clusters Correlation Clustering (CC) generates the partitioning of data points that optimizes a score function. CC was specifically designed for the situation where there are conflicts in edge labeling. Input: A graph where nodes correspond to references and edges between two nodes are labeled, in the simplest case, with +1 if there is evidence that u and v co-refer, and with −1 otherwise. CC finds a clustering that has the most agreement with the edge labeling, therefore limiting the conflicts as much as possible.

Experimental Evaluation Set Up: Dataset, Evaluation Metrics, Baseline Methods and Classifiers. Experiments on Web Domain: Base-level Algorithms, Comparison of various combination algorithms. The main difference among these algorithms are (a) the similarity metrics chosen to compare pairs of references and (b) the clustering algorithms that generate the final clusters based on similarities.

Efficiency: The running time of the ER ensemble algorithm consists of several parts: running base-level ER systems and loading the decisions by these systems on the edges, running the two regression classifiers to derive the context features, applying meta-level classification to predict the edge classes, and creating final clusters. Effects of Blocking: Blocking is important to improve the efficiency of our algorithms. It also has great impact on the quality of ER ensemble since the VCS level context features rely on the blocking.

Conclusion This paper develops a novel ER ensemble framework for combining multiple base-level entity resolution systems. The Context-Extended and Context-Weighted Ensemble approaches for meta-level classification that enable the framework to adapt to the dataset being processed based on the local context is also proposed. This study demonstrates the superiority of the Context- Weighted Ensemble approach.

Future Work of the Paper To explore principally different ways to create context features and a different classification scheme that trades quality for efficiency.

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