论文标题
预测原子候选猫头鹰类公理的分数
Predicting the Score of Atomic Candidate OWL Class Axioms
论文作者
论文摘要
候选公理评分是根据已知事实或数据提供的证据评估候选公理的可接受性的任务。自动架构或本体诱导需要可靠地评分候选公理的能力，但对于本体论和/或知识图验证也可能是有价值的。准确的公理评分启发式方法通常在计算上很昂贵，如果您想在迭代搜索技术中使用它们，例如水平生成和测试或进化算法，这是一个问题，这需要对大量候选公理进行评分。我们解决了开发预测模型作为预测候选公理可能得分的推理的替代品的问题，并且足够快，可以在这种情况下使用。为此，我们使用从本体论的集合结构中采取的语义相似性度量。我们表明，这项工作中提供的方法可以准确地学习候选猫头鹰类公理的可能性得分，并且可以为各种猫头鹰类公理而做到这一点。
Candidate axiom scoring is the task of assessing the acceptability of a candidate axiom against the evidence provided by known facts or data. The ability to score candidate axioms reliably is required for automated schema or ontology induction, but it can also be valuable for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often computationally expensive, which is an issue if you wish to use them in iterative search techniques like level-wise generate-and-test or evolutionary algorithms, which require scoring a large number of candidate axioms. We address the problem of developing a predictive model as a substitute for reasoning that predicts the possibility score of candidate class axioms and is quick enough to be employed in such situations. We use a semantic similarity measure taken from an ontology's subsumption structure for this purpose. We show that the approach provided in this work can accurately learn the possibility scores of candidate OWL class axioms and that it can do so for a variety of OWL class axioms.
