Solving and Explaining Analogy Questions Using Semantic Networks
Solving and Explaining Analogy Questions Using Semantic Networks
复制标题
使用语义网络解决和解释类比问题
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Chernova
中科院分区：
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作者：
Adrian Boteanu;S. Chernova
类比是一种基本的人类推理模式，依赖于关系相似性。理解类比是如何形成的有助于知识在不同语境之间的转移。在这项工作中提出的方法侧重于获得精确的解释类比。我们利用嘈杂的语义网络来回答和解释广泛的类比问题。我们的贡献的核心，语义相似性引擎，包括提取和比较图上下文的方法，揭示关系的并行性，类比是基于，同时减轻语义network.We的不确定性演示这些方法在两个任务：回答多项选择类比问题和生成人类可读的类比解释。我们在两个数据集上评估了我们的方法，共600个类比问题。我们的研究结果表明，可靠的性能和低假阳性率的问题回答;人类评估者同意我们的类比解释的96%。
Analogies are a fundamental human reasoning pattern that relies on relational similarity. Understanding how analogies are formed facilitates the transfer of knowledge between contexts. The approach presented in this work focuses on obtaining precise interpretations of analogies. We leverage noisy semantic networks to answer and explain a wide spectrum of analogy questions. The core of our contribution, the Semantic Similarity Engine, consists of methods for extracting and comparing graph-contexts that reveal the relational parallelism that analogies are based on, while mitigating uncertainty in the semantic network.We demonstrate these methods in two tasks: answering multiple choice analogy questions and generating human readable analogy explanations. We evaluate our approach on two datasets totaling 600 analogy questions. Our results show reliable performance and low false-positive rate in question answering; human evaluators agreed with 96% of our analogy explanations.