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Tables are a popular and efficient means of presenting structuredinformation. They are used extensively in various kinds of documents includingweb pages. Tables display information as a two-dimensional matrix, thesemantics of which is conveyed by a mixture of structure (rows, columns),headers, caption, and content. Recent research has started to consider tablesas first class objects, not just as an addendum to texts, yielding interestingresults for problems like table matching, table completion, or valueimputation. All of these problems inherently rely on an accurate measure forthe semantic similarity of two tables. We present TabSim, a novel method tocompute table similarity scores using deep neural networks. Conceptually,TabSim represents a table as a learned concatenation of embeddings of itscaption, its content, and its structure. Given two tables in thisrepresentation, a Siamese neural network is trained to compute a scorecorrelating with the tables' semantic similarity. To train and evaluate ourmethod, we created a gold standard corpus consisting of 1500 table pairsextracted from biomedical articles and manually scored regarding their degreeof similarity, and adopted two other corpora originally developed for adifferent yet similar task. Our evaluation shows that TabSim outperforms othertable similarity measures on average by app. 7% pp F1-score in a binarysimilarity classification setting and by app. 1.5% pp in a ranking scenario.

TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity