Understanding the semantic meaning of tabular data requires Entity Linking
(EL), in order to associate each cell value to a real-world entity in a
Knowledge Base (KB). In this work, we focus on end-to-end solutions for EL on
tabular data that do not rely on fact lookup in the target KB. Tabular data
contains heterogeneous and sparse context, including column headers, cell
values and table captions. We experiment with various models to generate a
vector representation for each cell value to be linked. Our results show that
it is critical to apply an attention mechanism as well as an attention mask, so
that the model can only attend to the most relevant context and avoid
information dilution. The most relevant context includes: same-row cells,
same-column cells, headers and caption. Computational complexity, however,
grows quadratically with the size of tabular data for such a complex model. We
achieve constant memory usage by introducing a Tabular Entity Linking Lite
model (TELL ) that generates vector representation for a cell based only on its
value, the table headers and the table caption. TELL achieves 80.8% accuracy on
Wikipedia tables, which is only 0.1% lower than the state-of-the-art model with
quadratic memory usage.

本文研究如何将表格数据与知识库中的实体进行实体链接。通过引入一个名为 Tabular Entity Linking Lite (TELL) 的模型，可以在不依赖于查询目标知识库中事实的情况下，以常数内存使用量为代价获得与当今最先进的基于注意力机制的模型相当的结果。