论文标题
朝向松散耦合知识图嵌入和基于本体的推理
Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning
论文作者
论文摘要
知识图完成（又称〜链接预测），即〜从知识图推断缺失信息的任务是许多应用程序中广泛使用的任务，例如产品建议和问题答案。知识图嵌入和/或规则挖掘和推理的最新方法是数据驱动的，因此仅基于输入知识图所包含的信息。这导致了不令人满意的预测结果，这使得这种解决方案不适用于关键领域，例如医疗保健。为了进一步提高知识图完成的准确性，我们建议将知识图嵌入数据驱动的数据驱动的能力与专家或累积制度（例如OWL2）引起的域特定于域的推理。通过这种方式，我们不仅可以通过域知识提高预测准确性，这些知识可能不包含在输入知识图中，而且还允许用户插入自己的知识图嵌入和推理方法。我们的最初结果表明，我们提高了香草知识图嵌入的MRR准确性，最多3倍，并且优于混合解决方案，这些溶液将知识图嵌入与规则挖掘和推理高达3.5倍MRR相结合。
Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion we propose to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or entailment regimes (e.g., OWL2). In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. Our initial results show that we enhance the MRR accuracy of vanilla knowledge graph embeddings by up to 3x and outperform hybrid solutions that combine knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.
