PeriodO 2: ‘Big Data’, Linked Data, and the reconciliation of absolute dates and traditional periodizations in archaeology
‘Big Data’ requires consistency in the structure and description of data from different sources, so that patterns in the same attributes can be identified across datasets. Unfortunately, archaeological datasets are notoriously inconsistent in both structure and terminology. Various attempts have recently been made to resolve this problem and enhance interoperability. One strategy that has worked well for the aggregation of spatially-situated data involves spatial gazetteers expressed as Linked Data; these allow heterogeneous data sources to align place information through the use of unique place identifiers maintained by a shared external reference. Temporal information, however, remains a problem, as archaeological chronology is often expressed in terms of interpretive periodizations rather than absolute dates. The PeriodO project seeks to translate natural-language period terms into machine-readable coordinates in time and space. The project’s first phase created a Linked Data gazetteer of period definitions, each including specific spatio-temporal boundaries explicitly stated by an authoritative source. Here we report on the second phase of the project, involving the implementation of PeriodO identifiers in large datasets. These identifiers make it possible not only to align period terms across datasets, but also to compare absolute dates with conventional period boundaries on a larger scale than ever before.
This Resource is Part of the Following Collections
- Society for American Archaeology 82nd Annual Meeting, Vancouver, BC (2017) •
- Frison Institute Symposium: The Future of "Big Data" in Archaeology
Cite this Record
PeriodO 2: ‘Big Data’, Linked Data, and the reconciliation of absolute dates and traditional periodizations in archaeology. Adam Rabinowitz, Ryan Shaw, Patrick Golden. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 429447)
Abstract Id(s): 16858