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与知识库一致的自然语言接口。

A Natural Language Interface Concordant with a Knowledge Base.

作者信息

Han Yong-Jin, Park Seong-Bae, Park Se-Young

机构信息

School of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea.

出版信息

Comput Intell Neurosci. 2016;2016:9174683. doi: 10.1155/2016/9174683. Epub 2016 Jan 21.

Abstract

The discordance between expressions interpretable by a natural language interface (NLI) system and those answerable by a knowledge base is a critical problem in the field of NLIs. In order to solve this discordance problem, this paper proposes a method to translate natural language questions into formal queries that can be generated from a graph-based knowledge base. The proposed method considers a subgraph of a knowledge base as a formal query. Thus, all formal queries corresponding to a concept or a predicate in the knowledge base can be generated prior to query time and all possible natural language expressions corresponding to each formal query can also be collected in advance. A natural language expression has a one-to-one mapping with a formal query. Hence, a natural language question is translated into a formal query by matching the question with the most appropriate natural language expression. If the confidence of this matching is not sufficiently high the proposed method rejects the question and does not answer it. Multipredicate queries are processed by regarding them as a set of collected expressions. The experimental results show that the proposed method thoroughly handles answerable questions from the knowledge base and rejects unanswerable ones effectively.

摘要

自然语言接口(NLI)系统可解释的表达式与知识库可回答的表达式之间的不一致是NLI领域中的一个关键问题。为了解决这个不一致问题,本文提出了一种将自然语言问题转换为可从基于图的知识库生成的形式化查询的方法。所提出的方法将知识库的子图视为形式化查询。因此,可以在查询之前生成与知识库中的概念或谓词相对应的所有形式化查询,并且还可以预先收集与每个形式化查询相对应的所有可能的自然语言表达式。自然语言表达式与形式化查询具有一对一的映射关系。因此,通过将问题与最合适的自然语言表达式进行匹配,将自然语言问题转换为形式化查询。如果这种匹配的置信度不够高,所提出的方法将拒绝该问题并且不进行回答。通过将多谓词查询视为一组收集的表达式来进行处理。实验结果表明,所提出的方法能够彻底处理来自知识库的可回答问题,并有效地拒绝不可回答的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c892/4745981/44174508c16b/CIN2016-9174683.001.jpg

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