Entity Matching on Web Tables: a Table Embeddings approach for Blocking

Entity matching, or record linkage, is the task of identifying records that refer to the same entity. Naive entity matching techniques (i.e., brute-force pairwise comparisons) have quadratic complexity. A typical shortcut to the problem is to employ blocking techniques to reduce the number of comparisons, i.e. to partition the data in several blocks and only compare records within the same block. While classic blocking methods are designed for data from relational databases with clearly defined schemas, they are not applicable to data from Web tables, which are more prone to noise and do not come with an explicit schema. At the same time, Web tables are an interesting data source for many knowledge intensive tasks, which makes record linkage on Web Tables an important challenge. In this work, we propose an unsupervised approach to partition the data, that does not exploit any external knowledge, but only relies on heuristics to select the blocking attributes. We compare different partitioning methods: we use (i) clustering on bagof-words, (ii) binning via Locality-Sensitive Hashing and (iii) clustering using word embeddings. In particular, the clustering methods show good results on a standard dataset of Web Tables, and, when combined with word embeddings, are a robust solution which allows for computing the clusters in a dense, low-dimensional space.