TBox learning from incomplete data by inference in BelNet+
TBox learning from incomplete data by inference in BelNet+
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DOI：
10.1016/j.knosys.2014.11.004
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Man Zhu;Zhiqiang Gao;Jeff Z. Pan;Yuting Zhao;Ying Xu;Zhibin Quan
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文献类型：
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作者：
Man Zhu;Zhiqiang Gao;Jeff Z. Pan;Yuting Zhao;Ying Xu;Zhibin Quan
在这项工作中，我们处理 TBox 从不完整的语义网络数据中学习的问题。 TBox，或概念模式，是描述逻辑（DL）本体的支柱，但总是很难获得。现有方法要么在不完整性的情况下无法获得正确的结果，要么学习的结果不足以解决不完整性问题。我们建议将深度学习中的 TBox 学习转化为贝叶斯描述逻辑网络（缩写为 BelNet+）扩展中的推理，从而在评估两个概念之间的关系时利用数据结构。 BelNet+ 将贝叶斯网络的概率推理能力与 DL 本体的逻辑形式主义（描述逻辑）相结合，支持有前途的推理。在本文中，我们首先解释了BelNet+的细节，并介绍了一种基于BelNet+的TBox学习方法。为了克服当前评估指标的缺点，我们提出了一种符合语义网中普遍提出的开放世界假设（OWA）的新颖评估框架。最后，与最先进的 TBox 学习器进行比较的实证研究结果验证了我们方法的有效性。
In this work we deal with the problem of TBox learning fromincompletesemantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to obtain. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies – Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.