Albert Meroño-Peñuela

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.

Semantic Web for the Humanities

Researchers have been interested recently in publishing and linking Humanities datasets following Linked Data principles. This has given rise to some issues that complicate the semantic modelling, comparison, combination and longitudinal analysis of these datasets. In this research proposal we discuss three of these issues: representation round-tripping, concept drift, and contextual knowledge. We advocate an integrated approach to solve them, and present some preliminary results.

LSD Dimensions: Use and Reuse of Linked Statistical Data

RDF Data Cube (QB) has boosted the publication of Linked Statistical Data (LSD) on the Web, making them linkable to other related datasets and concepts following the Linked Data paradigm. In this demo we present LSD Dimensions, a web based application that monitors the usage of dimensions and codes (variables and values in QB jargon) in Data Structure Definitions over six hundred public SPARQL endpoints. We plan to extend the system to retrieve more in-use QB metadata, serve the dimension and code data through SPARQL and an API, and provide analytics on the (re)use of statistical properties in LSD over time.

What Is Linked Historical Data

Datasets that represent historical sources are relative newcomers in the Linked Open Data (LOD) cloud. Following the standard LOD practices for publishing historical sources raises several questions: how can we distinguish between RDF graphs of primary and secondary sources? Should we treat archived and online RDF graphs differently in historical research? How do we deal with change and immutability of a triplified History? To answer these fundamental questions, we model historical primary and secondary sources using the OntoClean metaproperties and the theories of perdurance and endurance. We then use this model to give a definition of Linked Historical Data. We advocate a set of publishing practices for Linked Historical Data that preserve the ontological properties of historical sources.

An ecosystem for Linked Humanities Data

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. This paper presents QBer and the underlying structured data hub, which address the long tail of research data by catering for the needs of individual scholars. QBer 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 QBer, and evaluate our first results by showing how our system facilitates three use cases in socio-economic history.

The MIDI Linked Data Cloud

The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, linked datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. In this paper, we describe the MIDI Linked Data Cloud dataset, which represents multiple collections of digital music in the MIDI standard format as Linked Data using the novel midi2rdf algorithm. At the time of writing, our proposed dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI files, and provides Web-compatible descriptions of their MIDI events. We provide a comprehensive description of the dataset, and reflect on its applications for research in the Semantic Web and Music Information Retrieval communities.

The song remains the same: Lossless conversion and streaming of MIDI to RDF and back

In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

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.