WarSampo knowledge graph: Finland in the Second World War as Linked Open Data

Abstract

The Second World War (WW2) is arguably the most devastating catastrophe of human history, a topic of great interest to not only researchers but the general public. However, data about the Second World War is heterogeneous and distributed in various organizations and countries making it hard to utilize. In order to create aggregated global views of the war, a shared ontology and data infrastructure is needed to harmonize information in various data silos. This makes it possible to share data between publishers and application developers, to support data analysis in Digital Humanities research, and to develop data-driven intelligent applications. As a first step towards these goals, this article presents the WarSampo knowledge graph (KG), a shared semantic infrastructure, and a Linked Open Data (LOD) service for publishing data about WW2, with a focus on Finnish military history. The shared semantic infrastructure is based on the idea of representing war as a spatio-temporal sequence of events that soldiers, military units, and other actors participate in. The used metadata schema is an extension of CIDOC CRM, supplemented by various military history domain ontologies. With an infrastructure containing shared ontologies, maintaining the interlinked data brings upon new challenges, as one change in an ontology can propagate across several datasets that use it. To support sustainability, a repeatable automatic data transformation and linking pipeline has been created for rebuilding the whole WarSampo KG from the individual source datasets. The WarSampo KG is hosted on a data service based on W3C Semantic Web standards and best practices, including content negotiation, SPARQL API, download, automatic documentation, and other services supporting the reuse of the data. The WarSampo KG, a part of the international LOD Cloud and totalling ca. 14 million triples, is in use in nine end-user application views of the WarSampo portal, which has had over 690 000 end users since its opening in 2015.

Towards Interactive Analytics over RDF Graphs

“…The ability to leverage the inference rules of RDF Schema in the context of analytic queries is especially important for datasets which are described with ontologies that contain high number of subClassOf and subPropertyOf relationships for achieving semantic interoperability across various datasets, like those in the cultural and historic domain [7,42].…”

“…More and more data sources are being exported or produced using the Resource Description Framework (https://www.w3.org/RDF/) (or RDF, for short) standardized by the W3C. There are thousands of published RDF datasets (see [1] for a recent survey), including cross-domain knowledge bases (KBs) (e.g., DBpedia [2] and Wikidata [3]), domain specific repositories (e.g., DrugBank [4], GRSF [5], ORKG [6], WarSampo [7], and recently COVID-19 related datasets [8][9][10] as well as Markup data through schema.org.…”

Fast Cat

“…In the same context of historical research, Koho et al [12] present the WarSampo knowledge graph, a shared semantic infrastructure for publishing data about World War II and Finnish military history as Linked Data. The shared semantic infrastructure is based on the idea of representing war as a spatio-temporal sequence of events that soldiers, military units and other actors participate in.…”

Research on the construction of the semantic model for Chinese ancient architectures based on architectural narratives

“…There are many ontology models for specific fields and research objects. For example, the ABC ontology (Lagoze and Hunter, 2001) and the CIDOC CRM ontology (Doerr, 2003) in the cultural domain; in the history domain, there are the event-related linked data framework LODE (Shaw et al, 2009), the domain ontology of the history of China Three Kingdoms Period (Liao, 2017), the historical ontology of the Kuomintang and Communist collaboration in China (Lu, 2005) and the WarSampo data model for historical war (Koho et al, 2019); in the emergency response domain, there are the earthquake emergency case ontology (Cao, 2018), the biosecurity emergency case ontology (Wang, 2017) and the event-oriented natural disaster ontology (Ma et al, 2016); in the biomedicine domain, there are the clinical pathway ontology model (Ye et al, 2009) and the semantic modelling of pharmacogenomic knowledge (Boyce et al, 2013); and in the multimedia application domain, there are the multimedia event model E (Westermann and Jain, 2006) and the multimedia event model based on the six elements of news (5 W-1H) (Wang et al, 2007). These ontologies have analysed the constituent elements of the model.…”