Ashkan Ashkpour

Semantic technologies for historical research: A survey

During the nineties of the last century, historians and computer scientists created together a research agenda around the life cycle of historical information. It comprised the tasks of creation, design, enrichment, editing, retrieval, analysis and presentation of historical information with help of information technology. They also identified a number of problems and challenges in this field, some of them closely related to semantics and meaning. In this survey paper we study the joint work of historians and computer scientists in the use of Semantic Web methods and technologies in historical research. We analyse to what extent these contributions help in solving the open problems in the agenda of historians, and we describe open challenges and possible lines of research pushing further a still young, but promising, historical Semantic Web.

CEDAR: The Dutch Historical Censuses as Linked Open Data

In this document we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses, conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. The dataset contains more than 6.8 million statistical observations about the demography, labour and housing of the Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube vocabulary for multidimensional data, uses Open Annotation to express rules of data harmonization, and keeps track of the provenance of every single data point and its transformations using PROV. We link these observations to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC), which in turn link to DBpedia and GeoNames. The two main contributions of the dataset are the improvement of data integration and access for historical research, and the emergence of new historical data hubs, like classifications of historical religions and historical house types, in the Linked Open Data cloud.

The Aggregate Dutch Historical Censuses

Abstract Historical censuses have an enormous potential for research. In order to fully use this potential, harmonization of these censuses is essential. During the last decades, enormous efforts have been undertaken in digitizing the published aggregated outcomes of the Dutch historical censuses (1795–1971). Although the accessibility has been improved enormously, researchers must cope with hundreds of heterogeneous and disconnected Excel tables. As a result, the census is still for the most part an untapped source of information. The authors describe the main harmonization challenges of the census and how they work toward one harmonized dataset. They propose a specific approach and model in creating an interlinked census dataset in the Semantic Web using the Resource Description Framework technology.

Longitudinal Queries over Linked Census Data

This paper discusses the use of semantic technologies to increase quality, machine-processability, format translatability and cross-querying of complex tabular datasets. Our interest is to enable longitudinal studies of social processes in the past, and we use the historical Dutch censuses as case-study. Census data is notoriously difficult to compare, aggregate and query in a uniform fashion. We describe an approach to achieve this, discussing results, trade-offs and open problems.

The dataLegend ecosystem for historical statistics

The main promise of the digital humanities is the ability to perform scholarly studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections, produce scarce metadata, require expertise for effective integration, provide poor user support while producing mappings, and present issues at data access. This paper presents the datalegend platform, which addresses the long tail of research data by catering for the needs of individual scholars. datalegend allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present the architecture of datalegend; its core vocabularies and data; and QBer, an interactive, user supportive mapping generator and RDF converter. We evaluate our results by showing how our system facilitates use cases in socio-economic history.

Improving Access to the Dutch Historical Censuses with Linked Open Data

The Dutch Historical Censuses (1795-1971) contain statistics that describe almost two centuries of History in the Netherlands. These censuses were conducted once every 10 years (with some exceptions) from 1795 to 1971. Researchers have used its wealth of demographic, occupational, and housing information to answer fundamental questions in social economic history. However, accessing these data has traditionally been a time consuming and knowledge intensive task. In this paper, we describe the outcomes of the CEDAR project, which make access to the digitized assets of the Dutch Historical Censuses easier, faster, and more reliable. This is achieved by using the data publishing paradigm of Linked Data from the Semantic Web. We use a digitized sample of 2,288 census tables to produce a linked dataset of more than 6.8 million statistical observations. The dataset is modeled using the RDF Data Cube, Open Annotation, and PROV vocabularies. The contributions of representing this dataset as Linked Data are: (1) a uniform database interface for efficient querying of census data; (2) a standardized and reproducible data harmonization workflow; and (3) an augmentation of the dataset through richer connections to related resources on the Web.