论文标题
以事实英语创作知识
Knowledge Authoring with Factual English
论文作者
论文摘要
知识表示和推理(KRR)系统表示知识作为事实和规则的集合。像数据库一样,KRR系统包含有关工业企业,科学和业务等人类活动领域的信息。 KRR可以代表复杂的概念和关系,它们可以以复杂的方式查询和操纵信息。不幸的是,指定必要的知识需要大多数域专家没有的技能,而专业知识工程师很难找到,因此KRR技术受到了阻碍。一种解决方案可能是从英语文本中提取知识,并且许多作品都尝试这样做(Openseame,Google的吊索等)。不幸的是,目前,从不受限制的自然语言中提取逻辑事实仍然太不准确,无法用于推理,而限制语言语法(所谓的受控自然语言或CNL)对于用户来说很难学习和使用。然而,与其他方法相比,最近一些基于CNL的方法,例如知识创作逻辑机(KALM)的精度非常高,并且一个自然的问题是可以在多大程度上取消CNL限制。在本文中,我们通过将KALM框架移植到Mstanza神经自然语言解析器中来解决这个问题。在这里,我们将注意力限制在创作事实和查询上,因此我们的重点是我们所说的事实英语陈述。在我们的后续工作中将考虑创作其他类型的知识,例如规则。事实证明,基于神经网络的解析器有自己的问题,并且它们犯的错误范围从言论的一部分标记到lemmatization到依赖性错误。我们介绍了许多解决这些问题并测试新系统KALMFL(即KALM的事实语言)的技术,这些技术显示了许多基准,这些基准显示KALMFL的正确性超过了95%。
Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricting the grammar of the language (so-called controlled natural language, or CNL) is hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. In this paper, we address this issue by transplanting the KALM framework to a neural natural language parser, mStanza. Here we limit our attention to authoring facts and queries and therefore our focus is what we call factual English statements. Authoring other types of knowledge, such as rules, will be considered in our followup work. As it turns out, neural network based parsers have problems of their own and the mistakes they make range from part-of-speech tagging to lemmatization to dependency errors. We present a number of techniques for combating these problems and test the new system, KALMFL (i.e., KALM for factual language), on a number of benchmarks, which show KALMFL achieves correctness in excess of 95%.