The Archaeotools project: faceted classification and natural language processing in an archaeological context

Text Mining Oral Histories in Historical Archaeology

Advances in text mining and natural language processing methodologies have the potential to productively inform historical archaeology and oral history research. However, text mining methods are largely developed in the context of contemporary big data and publicly available texts, limiting the applicability of these tools in the context of historical and archaeological interpretation. Given the ability of text analysis to efficiently process and analyze large volumes of data, the potential for such tools to meaningfully inform historical archaeological research is significant, particularly for working with digitized data repositories or lengthy texts. Using oral histories recorded about a half-century ago from the anthracite coal mining region of Pennsylvania, USA, we discuss recent methodological developments in text analysis methodologies. We suggest future pathways to bridge the gap between generalized text mining methods and the particular needs of working with historical and place-based texts.

Labelling the past: data set creation and multi-label classification of Dutch archaeological excavation reports

The extraction of information from Dutch archaeological grey literature has recently been investigated by the AGNES project. AGNES aims to disclose relevant information by means of a web search engine, to enable researchers to search through excavation reports. In this paper, we focus on the multi-labelling of archaeological excavation reports with time periods and site types, and provide a manually labelled reference set to this end. We propose a series of approaches, pre-processing methods, and various modifications of the training set to address the often low quality of both texts and labels. We find that despite those issues, our proposed methods lead to promising results.

A study of semantic integration across archaeological data and reports in different languages

This study investigates the semantic integration of data extracted from archaeological datasets with information extracted via natural language processing (NLP) across different languages. The investigation follows a broad theme relating to wooden objects and their dating via dendrochronological techniques, including types of wooden material, samples taken and wooden objects including shipwrecks. The outcomes are an integrated RDF dataset coupled with an associated interactive research demonstrator query builder application. The semantic framework combines the CIDOC Conceptual Reference Model (CRM) with the Getty Art and Architecture Thesaurus (AAT). The NLP, data cleansing and integration methods are described in detail together with illustrative scenarios from the web application Demonstrator. Reflections and recommendations from the study are discussed. The Demonstrator is a novel SPARQL web application, with CRM/AAT-based data integration. Functionality includes the combination of free text and semantic search with browsing on semantic links, hierarchical and associative relationship thesaurus query expansion. Queries concern wooden objects (e.g. samples of beech wood keels), optionally from a given date range, with automatic expansion over AAT hierarchies of wood types and specialised associative relationships. Following a ‘mapping pattern’ approach (via the STELETO tool) ensured validity and consistency of all RDF output. The user is shielded from the complexity of the underlying semantic framework by a query builder user interface. The study demonstrates the feasibility of connecting information extracted from datasets and grey literature reports in different languages and semantic cross-searching of the integrated information. The semantic linking of textual reports and datasets opens new possibilities for integrative research across diverse resources.