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This paper reports on a quantitative study of data gathered from the Linked Open Vocabularies (LOV) catalogue, including the use of network analysis and metrics. The purpose of this paper is to gain insights into the structure of LOV and the use of vocabularies in the Web of Data. It is important to note that not all the vocabularies in it are registered in LOV. Given the de-centralised and collaborative nature of the use and adoption of these vocabularies, the results of the study can be used to identify emergent important vocabularies that are shaping the Web of Data.

The methodology is based on an analytical approach to a data set that captures a complete snapshot of the LOV catalogue dated April 2014. An initial analysis of the data is presented in order to obtain insights into the characteristics of the vocabularies found in LOV. This is followed by an analysis of the use of Vocabulary of a Friend properties that describe relations among vocabularies. Finally, the study is complemented with an analysis of the usage of the different vocabularies, and concludes by proposing a number of metrics.

The most relevant insight is that unsurprisingly the vocabularies with more presence are those used to model Semantic Web data, such as Resource Description Framework, RDF Schema and OWL, as well as broadly used standards as Simple Knowledge Organization System, DCTERMS and DCE. It was also discovered that the most used language is English and the vocabularies are not considered to be highly specialised in a field. Also, there is not a dominant scope of the vocabularies. Regarding the structural analysis, it is concluded that LOV is a heterogeneous network.

The paper provides an empirical analysis of the structure of LOV and the relations between its vocabularies, together with some metrics that may be of help to determine the important vocabularies from a practical perspective. The results are of interest for a better understanding of the evolution and dynamics of the Web of Data, and for applications that attempt to retrieve data in the Linked Data Cloud. These applications can benefit from the insights into the important vocabularies to be supported and the value added when mapping between and using the vocabularies.

Several recent statistical analyses provide overwhelming evidence for substantial injustice in immigration court decisions. Writers also explored the data for evidence of bias. Several ended with recommendations for more legal training for judges and more professional appellate review. These recommendations assume that the problem is in the interpretation of the law and conduct of the trial. My own experience has been that there is actually a greater problem in the interpretation of facts, at several levels. Courts provide for translators, but merely verbal translation is not enough. Cultural translation is required. In this chapter I illustrate what cultural translation is with instances from five different asylum cases that I have been involved in as an expert witness. I conclude with recommendations to support better use of this kind of information.