Predicting RDF triples in incomplete knowledge bases with tensor factorization
Predicting RDF triples in incomplete knowledge bases with tensor factorization
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DOI：
10.1145/2245276.2245341
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Lucas Drumond;Steffen Rendle;L. Schmidt-Thieme
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文献类型：
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作者：
Lucas Drumond;Steffen Rendle;L. Schmidt-Thieme
在 RDF 数据集上，当三元组的真值被明确说明或可以使用逻辑蕴涵推断时，它们的真值就是已知的。由于 RDF 的开放世界语义，对于既不在数据集中也无法逻辑推断的三元组的真值，我们无话可说。通过估计此类三元组的真值，人们可以从数据库中发现新信息，从而将查询范围扩大到可以回答的 RDF 库，支持知识工程师维护此类知识库或向用户推荐值得研究的资源等。在本文中，我们提出了一种新方法来预测任何 RDF 三元组的真值。我们的方法使用 RDF 知识库的 3 维张量表示，并应用考虑开放世界语义的张量分解技术，根据已经观察到的三元组来预测新的真实三元组。我们报告了在现实世界数据集上比较不同张量分解模型的实验结果。我们的实证结果表明，我们的方法在估计不完整 RDF 数据集的三重真值方面非常成功。
On RDF datasets, the truth values of triples are known when they are either explicitly stated or can be inferred using logical entailment. Due to the open world semantics of RDF, nothing can be said about the truth values of triples that are neither in the dataset nor can be logically inferred. By estimating the truth values of such triples, one could discover new information from the database thus enabling to broaden the scope of queries to an RDF base that can be answered, support knowledge engineers in maintaining such knowledge bases or recommend users resources worth looking into for instance. In this paper, we present a new approach to predict the truth values of any RDF triple. Our approach uses a 3-dimensional tensor representation of the RDF knowledge base and applies tensor factorization techniques that take open world semantics into account to predict new true triples given already observed ones. We report results of experiments on real world datasets comparing different tensor factorization models. Our empirical results indicate that our approach is highly successful in estimating triple truth values on incomplete RDF datasets.