Abstract
In commonsense knowledge representation, the Open World Assumption is adopted as a general standard strategy for the design, construction and use of ontologies, e.g. in OWL. This strategy limits the inferencing capabilities of any system using these ontologies because non-asserted statements could be assumed to be alternatively true or false in different interpretations. In this paper, we investigate the application of the Closed World Assumption to enable a better exploitation of the structural knowledge encoded in a SUMO-based ontology. To that end, we explore three different Closed World Assumption formulations for subclass and disjoint relations in order to reduce the ambiguity of the knowledge encoded in first-order logic ontologies. We evaluate these formulations on a practical experimentation using a very large commonsense benchmark automatically obtained from the knowledge encoded in WordNet through its mapping to SUMO. The results show that the competency of the ontology improves more than 47 % when reasoning under the Closed World Assumption. As conclusion, applying the Closed World Assumption automatically to first-order logic ontologies reduces their expressed ambiguity and more commonsense questions can be answered.
Abstract (translated)
在常识性知识表示中，采用开放世界假设作为本体的设计，构造和使用的一般标准策略，例如，在OWL。该策略限制了使用这些本体的任何系统的推理能力，因为在不同的解释中可以假设非断言语句可选地为真或假。在本文中，我们研究了封闭世界假设的应用，以便更好地利用基于SUMO的本体中编码的结构知识。为此，我们探索了三种不同的闭合世界假设公式，用于子类和不相交关系，以减少在一阶逻辑本体中编码的知识的模糊性。我们使用一个非常大的常识基准来评估这些公式，这些基准通过映射到SUMO从WordNet中编码的知识自动获得。结果表明，在闭合世界假设下推理时，本体的能力提高了47％以上。作为结论，将闭合世界假设自动应用于一阶逻辑本体可以减少它们表达的模糊性，并且可以回答更常见的问题。
URL
https://arxiv.org/abs/1808.04620
