论文标题
常识知识的选择策略
Selection Strategies for Commonsense Knowledge
论文作者
论文摘要
选择策略在一阶逻辑定理中广泛使用，证明可以选择大型知识库的那些部分，这些部分是证明当前定理的必要部分。通常，这些选择策略不会考虑符号名称的含义。在具有常识性知识的知识库中，通常选择符号名称具有含义，而这种含义为选择策略提供了宝贵的信息。我们介绍了基于向量的选择策略，这是一种基于单词嵌入的常识性知识的纯统计选择技术。为了证明定理的目的，我们比较了不同的常识性知识选择技术，并通过案例研究证明了基于向量的选择的有用性。
Selection strategies are broadly used in first-order logic theorem proving to select those parts of a large knowledge base that are necessary to proof a theorem at hand. Usually, these selection strategies do not take the meaning of symbol names into account. In knowledge bases with commonsense knowledge, symbol names are usually chosen to have a meaning and this meaning provides valuable information for selection strategies. We introduce the vector-based selection strategy, a purely statistical selection technique for commonsense knowledge based on word embeddings. We compare different commonsense knowledge selection techniques for the purpose of theorem proving and demonstrate the usefulness of vector-based selection with a case study.
