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.

Cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph have gained increasing attention over the last years and are starting to be deployed within various use cases. However, the content of such knowledge bases is far from being complete, far from always being correct, and suffers from deprecation (i.e. population numbers become outdated after some time). Hence, there are efforts to leverage various types of Web data to complement, update and extend such knowledge bases. A source of Web data that potentially provides a very wide coverage are millions of relational HTML tables that are found on the Web. The existing work on using data from Web tables to augment cross-domain knowledge bases reports only aggregated performance numbers. The actual content of the Web tables and the topical areas of the knowledge bases that can be complemented using the tables remain unclear. In this paper, we match a large, publicly available Web table corpus to the DBpedia knowledge base. Based on the matching results, we profile the potential of Web tables for augmenting different parts of cross-domain knowledge bases and report detailed statistics about classes, properties, and instances for which missing values can be filled using Web table data as evidence. In order to estimate the potential quality of the new values, we empirically examine the Local Closed World Assumption and use it to determine the maximal number of correct facts that an ideal data fusion strategy could generate. Using this as ground truth, we compare three data fusion strategies and conclude that knowledge-based trust outperforms PageRank- and voting-based fusion.