论文标题
对本体概念的量化和汇总
Quantification and Aggregation over Concepts of the Ontology
论文作者
论文摘要
我们认为，在某些KR应用中，我们希望量化以词汇中符号正式表示的一组概念集。我们表明，应将此定量与二阶定量和元编程定量区分开。我们还研究了与强化逻辑中概念的关系。 我们提出了一阶逻辑的扩展，以支持此类抽象，并表明它允许编写宽容的知识表达。 为了避免这种形式主义中的毫无意义的句子，我们完善了形成良好的句子的概念，并提出了一种用与公式中的令牌数量线性线性验证良好性的方法。 因此，我们已经扩展了FO（。）的知识表示语言和IDP-Z3，这是FO（。）的推理引擎。 我们表明，该扩展对于以耐耐受性的方式准确地对各种问题域进行建模至关重要。
We argue that in some KR applications, we want to quantify over sets of concepts formally represented by symbols in the vocabulary. We show that this quantification should be distinguished from second-order quantification and meta-programming quantification. We also investigate the relationship with concepts in intensional logic. We present an extension of first-order logic to support such abstractions, and show that it allows writing expressions of knowledge that are elaboration tolerant. To avoid nonsensical sentences in this formalism, we refine the concept of well-formed sentences, and propose a method to verify well-formedness with a complexity that is linear with the number of tokens in the formula. We have extended FO(.), a Knowledge Representation language, and IDP-Z3, a reasoning engine for FO(.), accordingly. We show that this extension was essential in accurately modelling various problem domains in an elaboration-tolerant way, i.e., without reification.
