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We thank Tosh et al. (1) for their interest in our research (2) but note that their analyses do not undermine the main findings of our article. Their suggestion that polity population divided by polity area should be one of the social complexity dimensions raises a number of issues. What does this ratio mean at large spatial scales, where populations are concentrated in large urban centers and much of the territory is not heavily populated? How are societies distributed across this variable and why? For example, a small-scale “simple” society could have a very high population density if it has access to a rich resource base. Tosh et al. (1) do not provide sufficient information or context to meaningfully … [↵][1]1To whom correspondence should be addressed. Email: t.currie{at}exeter.ac.uk. [1]: #xref-corresp-1-1

This paper describes OWL ontology re-engineering from the wiki-based social science codebook thesaurus developed by the Seshat: Global History Databank. The ontology describes human history as a set of over 1500 time series variables and supports variable uncertainty, temporal scoping, annotations and bibliographic references. The ontology was developed to transition from traditional social science data collection and storage techniques to an RDF-based approach. RDF supports automated generation of high usability data entry and validation tools, data quality management, incorporation of facts from the web of data and management of the data curation lifecycle. This ontology re-engineering exercise identified several pitfalls in modelling social science codebooks with semantic web technologies; provided insights into the practical application of OWL to complex, real-world modelling challenges; and has enabled the construction of new, RDF-based tools to support the large-scale Seshat data curation effort. The Seshat ontology is an exemplar of a set of ontology design patterns for modelling uncertainty or temporal bounds in standard RDF. Thus the paper provides guidance for deploying RDF in the social sciences. Within Seshat, OWL-based data quality management will assure the data is suitable for statistical analysis. Publication of Seshat as high-quality, linked open data will enable other researchers to build on it.