• Digital Humanities and Cultural Heritage
• 2017-2021close
• Publications close
• Research software close
• EU close
• CNR ExploRA close

This deliverable pertains to SSHOC Task 3.1 which was responsible for investigating and providing resources and tools to support the multilingual aspects of the future pan-EU SSH infrastructure. Making data and services accessible and usable in SSH is very much also a matter of providing relevant translations, translation of metadata concepts, multilingual vocabularies, terminology extraction across languages, multilingual databases. The deliverable offers a detailed report on the gathering and translation of relevant SSH metadata, ontologies and vocabularies for the use-cases indicated in the task���s topics: multilingual metadata concepts and vocabularies, the multilingual occupation ontology, with cross-country female occupational titles. In accordance with SSHOC and the EOSC FAIR recommendations and requirements, the metadata vocabularies and ontologies have been published via several different formats and facilities. Section 1. The introduction sets the landscape and describes the need of multilingual vocabularies both for classification and discovery in the context of a cloud-based infrastructure that will offer access to research data and related services adapted to the needs of the SSH community. Section 2. ���Multilingual metadata��� investigates the possibility to use and test Natural Language Processing (NLP) approaches and Machine Translation (MT) to make the metadata more accessible using national languages other than English. A selected case study was the recommended metadata set of the CLARIN Concept Registry (CCR): the whole set of metadata and definitions were translated into French, Greek, and Italian. The section describes the machine-translation and evaluation process, also comparing different technologies. Section 3. ���Multilingual vocabularies and ontologies��� introduces two other typical case-studies. The first one addresses one of the pressing needs in social sciences research. Many surveys, indeed, ask respondents to specify their occupation and the occupational ontology is used for the survey questions. For many languages the occupational titles for males and females are not identical. In section 3.1 the enrichment of the occupational ontology with lists for male and female titles, is described for many languages, namely for Dutch, German, Slovenian and French. The second case study focuses on the automatic extraction of terminology from texts: a list of domain- specific terms was automatically extracted from a corpus of Data Curation and Stewardship, validated by domain experts, automatically translated into multiple languages (Dutch, French, German, Greek, Italian, Slovenian) and linked to other existing terminologies. Section 4. describes the SKOS-ification and publication process of the results, together with the challenges posed by multilinguality. Section 5. offers an overview of the exploitation and sustainability of the results and how these are made available to the community. Finally the Conclusions provide some reflections on Machine Translation approaches adopted for translating the vocabularies into multiple languages, the advantages in terms of time saving and some first recommendations to the community.

In this article, the Mingei Online Platform is presented as an authoring platform for the representation of social and historic context encompassing a focal topic of interest. The proposed representation is employed in the contextualised presentation of a given topic, through documented narratives that support its presentation to diverse audiences. Using the obtained representation, the documentation and digital preservation of social and historical dimensions of Cultural Heritage are demonstrated. The implementation follows the Human-Centred Design approach and has been conducted under an iterative design and evaluation approach involving both usability and domain experts.

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a novel approach called Transformer Encoder Reasoning and Alignment Network (TERAN). TERAN enforces a fine-grained match between the underlying components of images and sentences, i.e., image regions and words, respectively, in order to preserve the informative richness of both modalities. TERAN obtains state-of-the-art results on the image retrieval task on both MS-COCO and Flickr30k datasets. Moreover, on MS-COCO, it also outperforms current approaches on the sentence retrieval task. Focusing on scalable cross-modal information retrieval, TERAN is designed to keep the visual and textual data pipelines well separated. Cross-attention links invalidate any chance to separately extract visual and textual features needed for the online search and the offline indexing steps in large-scale retrieval systems. In this respect, TERAN merges the information from the two domains only during the final alignment phase, immediately before the loss computation. We argue that the fine-grained alignments produced by TERAN pave the way towards the research for effective and efficient methods for large-scale cross-modal information retrieval. We compare the effectiveness of our approach against relevant state-of-the-art methods. On the MS-COCO 1K test set, we obtain an improvement of 5.7% and 3.5% respectively on the image and the sentence retrieval tasks on the Recall@1 metric. The code used for the experiments is publicly available on GitHub at https://github.com/mesnico/TERAN. Comment: Accepted in ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). arXiv admin note: text overlap with arXiv:2004.09144

Abstract In a conversational context, a user converses with a system through a sequence of natural-language questions, i.e., utterances. Starting from a given subject, the conversation evolves through sequences of user utterances and system replies. The retrieval of documents relevant to an utterance is difficult due to informal use of natural language in speech and the complexity of understanding the semantic context coming from previous utterances. We adopt the 2019 TREC Conversational Assistant Track ( CAsT ) framework to experiment with a modular architecture performing in order: (i) automatic utterance understanding and rewriting, (ii) first-stage retrieval of candidate passages for the rewritten utterances, and (iii) neural re-ranking of candidate passages. By understanding the conversational context, we propose adaptive utterance rewriting strategies based on the current utterance and the dialogue evolution of the user with the system. A classifier identifies those utterances lacking context information as well as the dependencies on the previous utterances. Experimentally, we evaluate the proposed architecture in terms of traditional information retrieval metrics at small cutoffs. Results demonstrate the effectiveness of our techniques, achieving an improvement up to 0.6512 ( + 201 % ) for P@1 and 0.4484 ( + 214 % ) for nDCG@3 w.r.t. the CAsT baseline.

Abstract In this paper we investigated the calculation of the anodic limit of two anions of ionic liquids, largely used as electrolyte of lithium batteries. Starting from a model based on calculations performed on single ions at the MP2 level of theory, we showed that the matching between calculation and experiments decreases while using more expanded basis set with respect to 6-31G**, possibly because of the destabilization of the neutral species when larger basis sets are considered. Additionally, in order to decrease the computational time, the performances for the calculation of the anodic limit obtained by means of a series of DFT functionals with increasing level of complexity (from the Generalized Gradient Approximation to the Range Separated Hybrid meta-Generalized Gradient Approximation) were compared. Overall, the best performing functionals are BMK, ωB97M-V and MN12-SX, while acceptable results can be obtained by M06-2X, M11, M08-HX and M11-L. Some less computationally expensive functionals, like CAM-B3LYP and ωB97X-D, also provide reasonable values of the anodic limit.

Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area.While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries.This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important – with the construction of a European Open Science Cloud as an audacious, ultimate goal – we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation. This article has been accepted for publication by EUP in the IJHAC: International Journal of Humanities and Arts Computing (https://www.euppublishing.com/loi/ijhac) International audience

Citation is a pillar for the construction of knowledge. By creating proper citations in a standardized way researchers can constitute a mesh of linked information for various purposes (from credit to reuse). This becomes increasingly important as the SSHOC Task 3.4 team confronts the realities of Social Sciences and Humanities Research in a digital age, when machine actionability takes on a renewed and vital importance. After conducting an inventory of data citation practices (SSHOC D3.2 "Inventory of SSH citation practices, and choice for SSHOC citation formats and implementation planning") and analysing the citation of data in DH1 2019 conference abstracts in order to build specifications for the citation prototype, the team discovered a very diverse landscape of data repositories. As a result, the team developed recommendations for citation in coordination with SSHOC Work Package 2 (Communication, Dissemination, and Impact), validated by external reviewers. These recommendations were used to guide a deeper analysis of citation practices in various SSH repositories and how they correspond to these recommendations in order to have a better idea of the current situation. This analysis was carried out in both a quantitative and qualitative fashion. For the qualitative part, the main goal was to describe, in detail, a selection of repositories representative of the SSH domain. The choice of repositories was made in collaboration with the SSHOC network in order to have a good representation of the very diverse contexts in SSH. This qualitative analysis focused on how these repositories were constructed to provide data citation services in detail. For the quantitative part, a list of repositories was already established by SSHOC Task 8.2 "Trust & Quality Assurance" and the team took this opportunity to establish synergies and extract a list of repositories to be checked according to defined criteria regarding data citation. The analysis checked 85 repositories from a list of 125 against a set of 7 criteria. In order to facilitate the work, the team used the citation viewer which is part of the prototype mentioned above. The main result of this quick study is that while there are positive signs, especially with respect to the use of landing pages and Persistent Identifiers (PIDs), there is quite a bit of room for improvement as a lot of repositories do not provide machine actionable information. This makes the prototype the Task 3.4 team is currently developing to create actionable citations all the more useful. It also appears from this work that it will be necessary to manually curate some citations in order to enrich them and make them actionable as the information is not always directly available (e.g., a landing page provides a link to a page which contains metadata expressed in another format). The result of this study will feed the development of the citation prototype developed in Task 3.4 and also liaise with SSHOC WP7 "Creating the SSH Open Marketplace" to integrate citations in SSH Open Marketplace2 with a "Cite As" property in the backend and Cite As box in the frontend interface. Another link exists with the very similar work currently being carried out in CLARIN for the Digital Object Gateway.