Automatically annotating column types with knowledge base (KB) concepts is a
critical task to gain a basic understanding of web tables. Current methods rely
on either table metadata like column name or entity correspondences of cells in
the KB, and may fail to deal with growing web tables with incomplete meta
information. In this paper we propose a neural network based column type
annotation framework named ColNet which is able to integrate KB reasoning and
lookup with machine learning and can automatically train Convolutional Neural
Networks for prediction. The prediction model not only considers the contextual
semantics within a cell using word representation, but also embeds the
semantics of a column by learning locality features from multiple cells. The
method is evaluated with DBPedia and two different web table datasets, T2Dv2
from the general Web and Limaye from Wikipedia pages, and achieves higher
performance than the state-of-the-art approaches.

本文提出了一个名为 ColNet 的神经网络列类型注释框架，它能够集成知识库推理和查找，自动训练卷积神经网络进行预测，不仅考虑了单元格内上下文语义，还从多个单元格中学习了列语义的局部特征，并在基于 DBPedia 和两个不同的 Web 表格数据集上进行了评估，并取得了比最先进方法更高的性能。

ColNet: 嵌入 Web 表格的语义，预测列类型

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

We study the problem of learning probabilistic first-order logical rules for
knowledge base reasoning. This learning problem is difficult because it
requires learning the parameters in a continuous space as well as the structure
in a discrete space. We propose a framework, Neural Logic Programming, that
combines the parameter and structure learning of first-order logical rules in
an end-to-end differentiable model. This approach is inspired by a
recently-developed differentiable logic called TensorLog, where inference tasks
can be compiled into sequences of differentiable operations. We design a neural
controller system that learns to compose these operations. Empirically, our
method outperforms prior work on multiple knowledge base benchmark datasets,
including Freebase and WikiMovies.

研究如何通过神经逻辑编程的框架来学习概率一阶逻辑规则应用于知识库推理，达到了比先前工作更好的效果。