A ‘historical case’ of Ontology-Based Data Access

Abstract

Historical research has steadily been adopting semantic technologies to tackle several recent problems in the field, such as making explicit the semantics contained in the historical sources, formalising them and linking them. Over the last decades, in social sciences and humanities an immense amount of new quantifiable data have been accumulated and made available in interchangeable formats, opening up new possibilities for solving old questions and posing new ones. This paper introduces a web-based platform to ease the access of scholars to historical and cultural data distributed across different data sources. The approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology). This work is focused on investigating the mechanisms and characteristics of the food production and commercial trade system during the Roman Empire.

Building Semantic Metadata for Historical Archives through an Ontology-driven User Interface

“…As we will see in Sections 4 and 5, ontology-supported plausibility checking, form menus, and autocompletion for textual input are precisely the tools adopted in PRiSMHA to generate the ontology-driven UI: we thus share with the authors the claim that this approach "ensures that the user only enters terms that the system understands and follows the idea of using ontologies as a shared vocabulary (Gruber, 1995)" (Paulheim and Probst 2010, p. 50). A large part of the literature about ontology-based UI concerns Ontology-Based Data Access; among many others, see (Calvanese et al, 2015) for an example in the historical domain, and (Soylu et al, 2017) that, besides proposing an ontology-based query formulation system, also contains a survey of such systems, including faceted search (Tunkelang and Marchionini, 2009) and query by navigation (see, for example, (Franconi et al, 2010), where ontology navigation is coupled with Natural Language Generation techniques).…”

“…Another research area that is relevant for PRiSMHA, although the issues it covers fall partially outside the main focus of this paper, is represented by semantic models used in Digital Humanities. In at least the last decade, computational ontologies, Linked Data, and in general approaches based on semantic models have been largely used to enhance access and management of Cultural Heritage datasets (Meroño-Peñuela et al, 2015;Oomen and Belice, 2012;Calvanese et al, 2015). Many initiatives demonstrate the interest for Semantic Web tools in the Digital Humanities: The huge European Union digital platform Europeana (www.europeana.eu) and projects like WarSampo (seco.cs.aalto.fi/projects/sotasampo/en) or PAPYRUS (Katifori et al, 2011) --using CIDOC Conceptual Reference Model (www.cidoc-crm.org) and Linked Open Data --among many others.…”

Using Ontology-Based Approach to Improved Information Retrieval Semantically for Historical Domain

“…The historical event ontology was based on the timeline that was created by Agricola network and being utilized as part of the semantic portal "CultureSampo-Finnish Culture on the Semantic Web", a cross-domain follow-up system of Museum Finland. This portal is an application of semantic technologies toward the development of e-culture portals that providing multimedia access to distributed collections of cultural heritage objects [15]. The classifications of events were based on the temporal timeline and other dimensions such as event types, i.e.…”

Ontop: Answering SPARQL queries over relational databases

“…10 For example, the EPNet project 11 relies on Ontop to improve access for scholars to historical and cultural data on food production and commercial trade system during the Roman Empire from several data sources [14]. Also, Ontop is used in Semantic Mediator [7], for accessing electronic health records [46], and for querying temporal and streaming data in OBDA [41].…”

“…else if n = UNION(n1, n2) then Once it finishes processing the leaves, it continues to the upper levels in the tree (lines [7][8][9][10][11][12][13][14][15][16][17], where the SPARQL operators (JOIN, OPTIONAL, UNION, FIL-TER, and PROJECT) are translated into the corresponding SQL operators (InnerJoin, LeftJoin, Union, Filter, and Project, respectively). Once the root is translated, the process is finished and the resulting SQL expression is returned.…”