An Effective Entity Resolution Approach for Big Data

Entity Resolution (ER) is defined as the process 0f identifying records/ objects that correspond to real-world objects/ entities. To define a good ER approach, the schema of the data should be well-known. In addition, schema alignment of multiple datasets is not an easy task and may require either domain expert or ML algorithm to select which attributes to match. Schema agnostic meta-blocking tries to solve such a problem by considering each token as a blocking key regardless of the attributes it appears in. It may also be coupled with meta-blocking to reduce the number of false negatives. However, it requires the exact match of tokens which is very hard to occur in the actual datasets and it results in very low precision. To overcome such issues, we propose a novel and efficient ER approach for big data implemented in Apache Spark. The proposed approach is employed to avoid schema alignment as it treats the attributes as a bag of words and generates a set of n-grams which is transformed to vectors. The generated vectors are compared using a chosen similarity measure. The proposed approach is a generic one as it can accept all types of datasets. It consists of five consecutive sub-modules: 1) Dataset acquisition, 2) Dataset pre-processing, 3) Setting selection criteria, where all settings of the proposed approach are selected such as the used blocking key, the significant attributes, NLP techniques, ER threshold, and the used scenario of ER, 4) ER pipeline construction, and 5) Clustering where the similar records are grouped into the similar cluster. The ER pipeline could accept two types of attributes; the Weighted Attributes (WA) or the Compound Attributes (CA). In addition, it accepts all the settings selected in the fourth module. The pipeline consists of five phases. Phase 1) Generating the tokens composing the attributes. Phase 2) Generating n-grams of length n. Phase 3) Applying the hashing Text Frequency (TF) to convert each n-grams to a fixed-length feature vector. Phase 4) Applying Locality Sensitive Hashing (LSH), which maps similar input items to the same buckets with a higher probability than dissimilar input items. Phase 5) Classification of the objects to duplicates or not according to the calculated similarity between them. We introduced seven different scenarios as an input to the ER pipeline. To minimize the number of comparisons, we proposed the length filter which greatly contributes to improving the effectiveness of the proposed approa