Querying Knowledge via Multi-Hop English Questions
Querying Knowledge via Multi-Hop English Questions
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DOI：
10.1017/s1471068419000103
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发表时间：
2019-09-01
影响因子：
1.4
通讯作者：
Kifer, Michael
中科院分区：
文献类型：
--
作者：
Gao, Tiantian;Fodor, Paul;Kifer, Michael
知识规范的固有困难和缺乏训练有素的专家是使基于知识表示和推理（KRR）范式的智能系统普及的一些主要障碍。使用自然语言，特别是受控自然语言（CNL）进行知识和查询创作是一种很有前途的方法，它可以使未经训练的逻辑学家的领域专家既创建正式知识又查询它。在之前的工作中，我们介绍了KALM系统（知识创作逻辑机），它以非常高的精度支持知识创作（和简单查询），这是目前通过机器学习方法无法实现的。本文对KALM的问答方面进行了扩展，并介绍了能够回答更复杂的英语问题的KALM- qa （KALM for question answer）。我们的研究表明，KALM-QA在一个名为MetaQA的广泛的电影相关问题套件上达到了100%的准确率，该套件包含近29,000个测试问题和超过260,000个培训问题。我们将其与已发表的机器学习方法进行对比，后者远远达不到这个高分。
The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.