Plausibility Assessment of Triples with Distant Supervision

Tracking #: 1753-2965

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Soon Hong
Mun Yong Yi

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Guest Editors ML4KBG 2016

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This paper reviews the process of triple validation, improving upon the knowledge base building and population process. This paper conceptualizes triple validation as a two-step procedure: a domain-independent plausibility assessment and a domain-dependent truth validation only for plausible triples. It also proposes a new plausible/nonsensical framework overlaid with a true/false framework. This paper focuses on the plausibility assessment of triples by challenging the limitations of existing approaches. It presents an unsupervised approach and attempts to consistently build both positive and negative training data with distant supervision by DBpedia and Wikipedia. It adopts instance-based learning to skip the generation of pre-defined models that have difficulty in dealing with triples’ various expressions. The experimental results support the proposed approach, which outperformed several unsupervised baselines. The proposed approach can be used to filter out newly extracted nonsensical triples and existing nonsensical triples in knowledge bases, as well as to learn even semantic relationships. The proposed approach can be used on its own, or it can complement existing truth-validation processes. Extending background knowledge for better coverage remains for future investigation.
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