Paper 3
Statistical Relation Cardinality Bounds in Knowledge BasesAuthors: Emir Muñoz, Matthias Nickles |
AbstractThere is an increasing number of Semantic Web knowledge bases (KBs) available on the Web, created in academia and industry alike. In this paper, we address the problem of lack of structure in these KBs due to their schema-free nature required for open environments such as the Web. Relation cardinality is an important structural aspect of data that has not received enough attention in the context of KBs. We propose a definition for relation cardinality bounds that can be used to unveil the structure that KBs data naturally exhibit. Information about relation cardinalities such as a person can have two parents and zero or more children, or a book should have one author at least, or a country should have more than two cities can be useful for data users and knowledge engineers when writing queries and reusing or engineering KB systems. Such cardinalities can be declared using OWL and RDF constraint languages as constraints on the usage of properties in the domain of knowledge; however, their declaration is optional and consistency with the instance data is not ensured. We first address the problem of mining relation cardinality bounds by proposing an algorithm that normalises and filters the data to ensure the accuracy and robustness of the mined cardinality bounds. Then we show how these bounds can be used to assess two relevant data quality dimensions: consistency and completeness. Finally, we report that relation cardinality bounds can also be used to expose structural characteristics of a KB by mapping the bounds into a constraint language to declare the actual shape of data. Keywords: Semantic web, Knowledge bases, RDF Cardinality bounds, Data shapes. |