Inducing semantic relations from conceptual spaces: A data-driven approach to plausible reasoning
Inducing semantic relations from conceptual spaces: A data-driven approach to plausible reasoning
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DOI：
10.1016/j.artint.2015.07.002
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
J. Derrac;Steven Schockaert
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文献类型：
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作者：
J. Derrac;Steven Schockaert
事实证明，插值和更高推理等常识推理模式对于处理结构化知识库中的差距很有用。在实践中应用这些推理模式的一个重要困难是，它们依赖于不同概念和实体如何在语义上相关的细粒度知识。在本文中，我们展示了如何所需的语义关系，可以从大量的文本文档的集合中学习。为此，我们首先从文本文档中归纳出一个概念空间，使用多维缩放。然后，我们依赖于关键的洞察力，所需的语义关系对应于定性的空间关系，在这个概念空间。除此之外，在一个完全无监督的方式，我们确定在概念空间中的显着方向，对应于可解释的相对属性，如“更多的水果比”（在空间的葡萄酒），从而在一个象征性的和可解释的表示的概念空间。为了评估我们的语义关系的质量，我们展示了它们如何可以利用一些常识推理为基础的分类。我们的实验表明，这些分类器可以优于标准的方法，同时能够提供直观的解释分类决策。一些众包实验提供了进一步的见解提取的语义关系的性质。
Commonsense reasoning patterns such as interpolation and a fortiori inference have proven useful for dealing with gaps in structured knowledge bases. An important difficulty in applying these reasoning patterns in practice is that they rely on fine-grained knowledge of how different concepts and entities are semantically related. In this paper, we show how the required semantic relations can be learned from a large collection of text documents. To this end, we first induce a conceptual space from the text documents, using multi-dimensional scaling. We then rely on the key insight that the required semantic relations correspond to qualitative spatial relations in this conceptual space. Among others, in an entirely unsupervised way, we identify salient directions in the conceptual space which correspond to interpretable relative properties such as ‘more fruity than’ (in a space of wines), resulting in a symbolic and interpretable representation of the conceptual space. To evaluate the quality of our semantic relations, we show how they can be exploited by a number of commonsense reasoning based classifiers. We experimentally show that these classifiers can outperform standard approaches, while being able to provide intuitive explanations of classification decisions. A number of crowdsourcing experiments provide further insights into the nature of the extracted semantic relations.