# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 3 Motivation for OWL vs. XML  
There are many languages that could satisfy the requirements outlined in the previous section. This section discusses the motivation behind the selection of the two approaches evaluated in this paper.  
First, we were interested in evaluating how the semantics affects the performance with respect to data modeling and the querying of cognitive radio capabilities. Since OWL has computer processable semantics (i.e., it belongs to a group of _formal languages_--languages with formal syntax and formal semantics) [34], the consistency of device descriptions represented in OWL can be checked first. Then, new facts can be derived from a given set of facts using the inference rules executed by an inference engine. The derived facts are guaranteed to be consistent with the knowledge base, i.e., its derivation process is sound. In contrast, other languages such as XML Schema Definition Language (XSD) [35; 36]/XML or relation/tuple are either semi-structured or structured and do not have much of the semantic richness, require all facts about devices to be explicitly expressed, and cannot generate inferred facts without dedicated software. Therefore, the OWL-based approach was chosen as a baseline that plays a significant role in the matching process, especially in establishing the query ground truth for the evaluation of the quality of the matching results.  
Second, we considered the XML-based approach rather than other approaches, such as the relational table approach, since the former better addresses the language requirements defined by the use cases described in Section 1.1. XML is more flexible than the relational table approach with respect to the updating of the structure of data. Whenever there is a need to update the structure of the data, it is much easier to make changes to the XML data than to a relational table, since the latter would require a change in the relational database schema, which would have to be followed by the restructuring of the whole database. Additionally, XML data are self-describing, while relational data are not [37]. An XML document contains not only the data, but also a tagging for the data that represent what it is [37]. With the relational model, the content of the data is defined by its column definition [37]. All data in a column must have the same type of data [37]. The flexibility feature is very critical in our problem, since the data representation language for our use cases should be flexible enough to deal with various data updates, especially for cases where devices with new types or capabilities are registered with the system. Therefore, in this paper, we only considered OWL-based and XML-based approaches as being appropriate to address the problem.  
Each of these two approaches uses other languages for representing data and queries. The languages used in the two approaches are listed in Table 2. They are categorized into three levels [38]: (a) languages for modeling background knowledge (the schema level); (b) languages for representing device descriptions (the data level); and (c) languages for requests for services (the query level).