期刊
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
卷 -, 期 -, 页码 930-937出版社
IEEE
DOI: 10.1109/BigData50022.2020.9378077
关键词
Table Similarity Search; Similarity Measure; Machine Learning; Deep Learning
类别
资金
- Deutsche Forschungsgemeinschaft (DFG) [LE 1428/7-1, STA 1471/1-1]
Tables are a popular and efficient means of presenting structured information. They are used extensively in various kinds of documents including web pages. Tables display information as a two-dimensional matrix, the semantics of which is conveyed by a mixture of structure (rows, columns), headers, caption, and content. Recent research has started to consider tables as first class objects, not just as an addendum to texts, yielding interesting results for problems like table matching, table completion, or value imputation. All of these problems inherently rely on an accurate measure for the semantic similarity of two tables. We present TabSim, a novel method to compute table similarity scores using deep neural networks. Conceptually, TabSim represents a table as a learned concatenation of embeddings of its caption, its content, and its structure. Given two tables in this representation, a Siamese neural network is trained to compute a score correlating with the tables' semantic similarity. To train and evaluate our method, we created a gold standard corpus consisting of 1500 table pairs extracted from biomedical articles and manually scored regarding their degree of similarity, and adopted two other corpora originally developed for a different yet similar task. Our evaluation shows that TabSim outperforms other table similarity measures on average by app. 7% pp F1-score in a binary similarity classification setting and by app. 1.5% pp in a ranking scenario. The codes and resources introduced here are available at https://github.com/Marhabibi/TabSim.
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