Move Cultural Heritage Knowledge Graphs in Everyone's Pocket

Tracking #: 3117-4331

Maria Angela Pellegrino
Vittorio Scarano
Carmine Spagnuolo

Responsible editor: 
Special Issue Cultural Heritage 2021

Submission type: 
Full Paper
Last years witnessed a shift from the potential utility in digitization to a crucial need to enjoy activities virtually by noting that while in the past (before 2019) data curators recognised the utility in performing data digitization, during the COVID-19, due to the lockdown, no one could enjoy Cultural Heritage in person and it required a great investment in remotely offering activities to make culture survive. The Cultural Heritage community heavily invested in digitization campaigns, mainly modeling data as Knowledge Graphs by becoming one of the most successful application domains of the Semantic Web technologies. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers' technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. Accessible to all underlines that we propose a software framework freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers' technical challenges, carefully considered both in the design and in the evaluation phases. This article, first, analyzes the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data on which developers can rely in designing and implementing data exploitation tools in this domain. Moreover, we point out data aspects and challenges that developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation on a standard benchmark of question-answering systems. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.
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Solicited Reviews:
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Review #1
Anonymous submitted on 11/May/2022
Review Comment:

The authors have made the minor corrections I proposed during te previous round. I think this paper can now be accepted for the specail issue.

Review #2
Anonymous submitted on 15/May/2022
Review Comment:

This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include (1) originality, (2) significance of the results, and (3) quality of writing. Please also assess the data file provided by the authors under “Long-term stable URL for resources”. In particular, assess (A) whether the data file is well organized and in particular contains a README file which makes it easy for you to assess the data, (B) whether the provided resources appear to be complete for replication of experiments, and if not, why, (C) whether the chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate for long-term repository discoverability, and (4) whether the provided data artifacts are complete. Please refer to the reviewer instructions and the FAQ for further information.

The authors have addressed all the points raised by the reviewers in the previous version.