This paper describes EPROP, a novel technique requiring little prior knowledge for word sense disambiguation of semantic relations between pairs of ambiguous concepts in knowledge bases. Our method makes inferences by aggregating evidences from ambiguous word interpretations and propagating the acquired knowledge over a taxonomy to generalize or specialize this knowledge. This propagation process allows the estimation of the degree of belief for each possible word sense assignment given the available evidence. EPROP only requires a sense inventory structured as a taxonomy to disambiguate a knowledge base by combining evidence from the ambiguous facts stored in the knowledge base. We have performed different experiments that show that our method achieves good results on the disambiguation of the semantic relations included in WordNet and ConceptNet. We also show how our method can be used to improve the performance of state-of-the-art word sense disambiguation methods.

中文翻译：

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根据意义清单使用证据聚合消除语义关系歧义


本文介绍了 EPROP，这是一种无需先验知识即可对知识库中模糊概念对之间的语义关系进行词义消歧的新技术。我们的方法通过聚合来自歧义单词解释的证据并将所获得的知识传播到分类学上以概括或专门化这些知识来进行推论。该传播过程允许在给定可用证据的情况下估计每个可能的词义分配的置信度。 EPROP 仅需要一个作为分类法构建的意义清单，通过结合来自存储在知识库中的模糊事实的证据来消除知识库的歧义。我们进行了不同的实验，结果表明我们的方法在消除 WordNet 和 ConceptNet 中语义关系的歧义方面取得了良好的效果。我们还展示了如何使用我们的方法来提高最先进的词义消歧方法的性能。