Abstract
The Archaeology Data Service (ADS) has a mandate to provide a digital repository for outputs from research funded by AHRC, NERC, English Heritage and other bodies. Archaeology has seen increasing use of the Web in recent years for data dissemination, and the ADS holds a wide range of datasets from archaeological excavations. However datasets and applications are currently fragmented and isolated. Different terminologies and data structures hinder search and comparison across datasets. Because of these impediments, archaeological data can be hard to reuse and re-examine in light of evolving research questions and interpretations. In an attempt to address this, the ADS have begun to ingest some of its excavation data into a triple store and expose it as linked data. This paper will briefly discuss the STAR and STELLAR projects which led up to the development of ADS linked data and will also outline the technologies used to develop it. In particular, it will discuss the more practical details of creating the triple store, populating it with excavation data in RDF format, and finally publishing it as linked data. Finally, using the ADS linked data as an example dataset, an overview of possible future directions will be outlined, in particular to explore how we can enrich the existing and forthcoming linked data with both
archaeological and non-archaeological data.
archaeological and non-archaeological data.
Original language | English |
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Title of host publication | Archaeology in the Digital Era |
Subtitle of host publication | Papers from the 40th Annual Conference of Computer Applications and Quantitative Methods in Archaeology (CAA), Southampton, 26-29 March 2012 |
Editors | Graeme Earl, Tim Sly, Angeliki Chrysanthi, Patricia Murrieta-Flores, Constantinos Papadopoulos, Iza Romanowska, David Wheatley |
Publisher | Amsterdam University Press |
Pages | 216-223 |
Number of pages | 8 |
ISBN (Electronic) | 978 90 4851 959 0, 978 90 4851 960 6 |
ISBN (Print) | 978 90 8964 663 7 |
Publication status | Published - 2013 |
Keywords
- Linked Open Data
- Semantic Web
- Excavation Data
- cidoc crm