Computational ontologies for accessing, controlling, and dissemination of knowledge in the cultural heritage sector

⦁ Roberto, J. (2021). Computational ontologies for accessing, controlling, and disseminating knowledge in the cultural heritage sector: a case study. In Shane Hawkins (Ed.). Access and Control in Digital Humanities (pp. 61-77). New York: Routledge, ISBN 9780367201012.

Access, Control, and Dissemination in Digital Humanities.

While DH is seen by some as especially interdisciplinary or more conducive to group work, linked data, and open research, including both access to results and participation in research itself, the very nature of its connectedness creates challenges for researchers who wish to assert control of data, have some role in how data is used or how work is acknowledged, and how it is attributed and recorded. Researchers involved in any substantial DH project must confront similar questions: who should be allowed to make reproductions of artifacts, which ones, how many, how often, of what quality and at what cost, what are the rights of possession and reproduction, including access, copyright, intellectual property rights or digital rights management. Given the potential of open and accessible data, it is sometimes suggested that DH might be a much-needed bridge between ivory tower institutions and the general public. The promise of DH in this regard, however, still remains in many ways unfulfilled, raising the question of who DH is for, if not solely for bodies of like-minded academics.

Contributors to this volume have varied experiences with applications for digital technology in the classroom, in museums and archives, and with the general public and they present answers to these problems from a variety of perspectives. Digital Humanities is not a homogeneous enterprise, and we find that DH functions differently in different fields across the humanities and is put to different ends with varying results. As a result, one may already (fore)see DH moving in distinct directions in individual academic fields, but whether this splintering will have a positive effect or is an indication that disciplines are retreating to their respective silos, remains to be seen. We need to understand better how such differences are communicated among various fields, and how those results are adopted, not to mention evaluated, and by whom. This volume addresses these issues with concrete examples from researchers in the field.

Computational ontologies for accessing, controlling, and dissemination of knowledge in the cultural heritage sector

Cultural heritage is rich in associations. Museum artworks contain semantically rich information that configures a semantic network: a collection of items has features and are related to another collection of items. For example, “The Physical Impossibility of Death in the Mind of Someone Living” is a piece of Conceptual Art created by Damien Hirst, a leading member of the “Young British Artists” (YBAs). YBAs are influenced by Minimalism and Conceptual Art. This piece of art is related to Charles Saatchi (collector), Steven A. Cohen (owner) and the “shark” (artwork) of Eddie Saunders. This “semantic network” is not limited to a single collection but also spans over other related collections in different museums. Nowadays, most of this information about art collections is often embedded within databases or within highly textual documents. This is a problem because it is difficult to extract, re-use, interpret, correlate or compare the relevant information expressed implicitly in semi-structured and unstructured resources. Motivated by the above observation, researchers in Digital Humanities are working with computational ontologies to extract the implicit knowledge embedded in textual resources and to make heterogeneous museum collections semantically interoperable.

In general, an ontology is a form of knowledge conceptualization that makes this knowledge publicly available and reusable. From a technological point of view, a computational ontology is a collection of statements written in a formal language. Its purpose is to establish the relations between different concepts and specify logical rules for reasoning about them. Common components of ontologies include concepts (e.g. “Abstract Expressionism”), properties (e.g. “Abstract Expressionism is not focused on figures or imagery”) and relations (e.g. “Action Painting is an instance of the metaclass Abstract Expressionism”). Ontologies offer enhanced representation capabilities and they can also support reasoning operations that are at least partially similar to human reasoning. Through the use of a reasoner, it is possible to derive new facts from the existing ontologies. Reasoner is a software that works by inferring logical consequences from a set of explicitly asserted facts or axioms. Thus, ontologies allow us to make explicit domain assumptions: e.g. “Abstract Expressionism has many stylistic similarities to the Russian artists of the early 20th century”.

In this chapter, we explore how computational ontologies make feasible the process of accessing, controlling, and dissemination of knowledge feasible in the cultural heritage sector. This chapter is divided into three major sections. The first section explains what an ontology is and how to extract ontologies from unstructured texts. Specifically, we analyze the extraction and population of ontologies by applying natural language analysis techniques to texts. The second section presents current research in ontology learning applied to the cultural heritage sector. We show concrete examples of available ontologies for museums, ontologies that have been developed for describing museum artefacts and objects, quantitative analyzes of the art market using ontologies, web ontologies for modelling art collections and new methods to provide personalized tour recommendation for museum visits. In the last section, we will put special attention on the Europeana ontology, which is the EU digital platform for cultural heritage.