- (Sarawagi and Bhamidipaty, 2002) ⇒ Sunita Sarawagi, Anuradha Bhamidipaty. (2002). “Interactive Deduplication Using Active Learning.” In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2002. doi:10.1145/775047.775087.
Deduplication is a key operation in integrating data from multiple sources. The main challenge in this task is designing a function that can resolve when a pair of records refer to the same entity in spite of various data inconsistencies. Most existing systems use hand-coded functions. One way to overcome the tedium of hand-coding is to train a classifier to distinguish between duplicates and non-duplicates. The success of this method critically hinges on being able to provide a covering and challenging set of training pairs that bring out the subtlety of deduplication function. This is non-trivial because it requires manually searching for various data inconsistencies between any two records spread apart in large lists. We present our design of a learning-based deduplication system that uses anovel method of interactively discovering challenging training pairs using active learning. Our experiments on real-life datasets show that active learning significantly reduces the number of instances needed to achieve high accuracy. We investigate various design issues that arise in building a system to provide interactive response, fast convergence, and interpretable output.
- Our contribution
- We designed a learning based deduplication system (ALIAS - Active Learning led Interactive Alias Suppression) that allows automatic construction of the deduplication function by using anovel method of interactively discovering challenging training pairs. Our key insight is to simultaneously build several redundant functions and exploit the disagreement amongst them to discover new kinds inconsistencies amongst duplicates in the dataset. Active learning [6, 8] methods also rely on a similar insight for selecting instances for labeling from a large pool of unlabeled instances. Unlike an ordinary learner that trains using a static training set, an active learner actively picks subsets of instances which when labeled will provide the highest information gain to the learner.
With this approach the more difficult task of bringing together the potentially confusing record pair s is automated by the learner. The user has to only perform the easy task of labeling the selected pairs of records as duplicate or not.
We designed an active learning algorithm that can meet our design goals of interactive response, fast convergence, and high accuracy. Finally, our system outputs a deduplication function that is easy to interpret and efficient to evaluate when deployed on large record lists. This required evaluating various non-obvious design tradeoffs that arise when using current active learning methods in a practical setting. Experiments on real-life datasets show that our approach reduces the number of labeled training pairs by two orders of magnitude to reach a certain accuracy. After labeling100 pairs selected interactively by our system, the learnt deduplication function can achieve the peak accuracy which a randomly chosen set of pairs cannot achieve even with 7000 pairs.
Figure 1 shows the overall design of our ALIAS system for deduplication. There are three primary inputs to the system:
- . Database of records (D) The original set D of records in which duplicates need to be detected. The data has d attributes a l, . . . a d, each of which could be textual or numeric. The goal of the system is to find the subset of pairs in the cross-product D x D that can be labeled as duplicates.
- . Initial training pairs (L) An optional small(less than ten) seed L of training records arranged in pairs of duplicates or non-duplicates.
- . Similarity functions (.T) A set .~ of n I functions each of which computes a similarity match between two records rl, r2 based on any subset of d attributes. Examples of such functions are edit-distance, soundex, abbreviationmatch on text fields, and absolute difference for integer fields. Many of the common functions could be inbuilt and added by default based on the data type. However, it is impossible to totally obviate an expert's domain knowledge in designing specific matching functions. These functions can be coded in the native language of the system (C++ in our case) and loaded dynamically. The functions in the set can be highly redundant and unrelated to each other because finally our automated learner wil perform the nontrivial task of finding the right
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|2002 InteractiveDeduplication||Sunita Sarawagi|
|Interactive Deduplication Using Active Learning||Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2002||http://www.it.iitb.ac.in/~sunita/papers/kdd02.pdf||10.1145/775047.775087||2002|
|Author||Sunita Sarawagi + and Anuradha Bhamidipaty +|
|journal||Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2002 +|
|title||Interactive Deduplication Using Active Learning +|