• DARIAH EU
• 2012-2021close
• Conference object close
• European Commission close
• EC|FP7 close

TRAME is a research infrastructure for medieval manuscripts. The TRAME engine scans a set of sources for searched terms and retrieves links to a wide range of possible information, from simple reference, to detailed manuscript record, to full text transcriptions. Currently, it is possible to perform queries by: free-text, shelfmark, author, title, date, copyst or incipit, on more than 80 selected scholarly digital resources across EU and USA. Since 2014 September 1st, TRAME has entered a new phase and the current work is focused on: extending the meta-search approach to other web resources, leveraging the users interaction to define an ontology for medieval manuscripts, re-designing the front-end towards a new UX approach.

This study aims to improve the knowledge of polychrome Roman sarcophagi by showing the first observations about the colour palette detected and the painting techniques by combining archaeological research with scientific results. These results are the base to understand the ancient colour and to improve the virtual reconstruction through the use of 3D Computer Graphic.

In this paper we describe the development and evaluation of a visual analytics tool to support historical research. Historians continuously gather data related to their scholarly research from archival visits and background search. Organising and making sense of all this data can be challenging as many historians continue to rely on analog or basic digital tools. We built an integrated note-taking environment for historians which unifies a set of func-tionalities we identified as important for historical research including editing, tagging, searching, sharing and visualization. Our approach was to involve users from the initial stage of brainstorming and requirement analysis through to design, implementation and evaluation. We report on the process and results of our work, and conclude by reflecting on our own experience in conducting user-centered visual analytics design for digital humanities. International audience

This paper discusses the registry developed by the ARIADNE project for describing the archaeological resources that are made available by the partners of the project for the purposes of discovery, access and integration on a research infrastructure. These resources include: data, services and language resources, such as metadata formats, vocabularies and mappings. The registry is addressed to cultural institutions, private or public, which wish to describe their assets in order to make them known to e-infrastructures.

Narrative is emerging as a notion that may enable overcoming the limitations of the discovery functionality (only ranked lists of objects) offered by information systems to their users. We present preliminary results on modelling narratives by means of formal ontology, by introducing a conceptualization of narratives and a mathematical expression of it. Our conceptualization tries to capture fundamental notions of narratives as defined in narratology, such as fabula, narration and plot. A validation of the conceptualization and of its mathematical specification is ongoing, based on the Semantic Web standards and on the CIDOC CRM ISO standard ontology.

Abstract. The remote visualization and navigation of 3D data directly inside the web browser is becoming a viable option, due to the recent efforts in standardizing the components for 3D rendering on the web platform. Nevertheless, handling complex models may be a challenge, especially when a more generic solution is needed to handle different cases. In particular, archeological and architectural models are usually hard to handle, since their navigation can be managed in several ways, and a completely free navigation may be misleading and not realistic. In this paper we present a solution for the remote navigation of these dataset in a WebGL component. The navigation has two possible modes: the ”bird’s eye” mode, where the user is able to see the model from above, and the ”first person” mode, where the user can move inside the structure. The two modalities are linked by a point of interest, that helps the user to control the navigation in an intuitive fashion. Since the terrain may not be flat, and the architecture may be complex, it’s necessary to handle these issues, possibly without implementing complex mesh-based collision mechanisms. Hence, a complete navigation is obtained by storing the height and collision information in an image, which provides a very simple source of data. Moreover, the same image-based approach can be used to store additional information that could enhance the navigation experience. The method has been tested in two complex test cases, showing that a simple yet powerful interaction can be obtained with limited pre-processing of data.

Online publishing of almost every type of 3D data has become a quasi-standard routine. Nevertheless, the integration in a web page of a single 3D model, or of a predefined restricted set of models, raises different issues compared to an efficient and effective integration of thousands of them in an online repository. In this case it is mandatory to have an automatized pipeline to prepare and homogenize the dataset. The pipeline should be able to automatically wrap 3D data in all conditions, and display every single model with the best scene setup without any (or with a minimal) interaction by the database maintainers. This paper, retracing the steps of a recent real application case, aims at showing all the faced issues (and the adopted solutions) to publish a large and heterogeneous three-dimensional dataset in a web specialized repository. We want to introduce a valid and reusable strategy, starting from the description of the pipeline adopted for data pre-processing and moving to the choices made in the 3D viewer implementation. The paper concludes with a discussion on the actual state of the integration of 3D data with the other multimedia informative layers.

In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of archaeological knowledge, expressed as knowledge graph, and which form would best meet the communities' needs. A case study was held which involved the user-driven mining of generalized association rules. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant by domain experts.

CENDARI (Collaborative European Digital Archive Infrastructure) is a research infrastructure project aimed at integrating digital archives and resources for research on medieval and modern European history.The project brings together information and computer scientists with historians and existing historical research infrastructures (archives, libraries, other digital projects) to improve conditions for digital historical scholarship. CENDARIhas engaged in extensive networking with the archives and libraries of Europe, especially those in Eastern Europe.CENDARI is a 4-year, European-Commission-funded project led by Trinity College Dublin, in partnership with 14 institutions across 8 countries. International audience

Recent years have seen the emergence of graph-based Knowl-edge Bases build upon Semantic Web technologies, known as KnowledgeGraphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of thisability, amongst which are scalability and uncertainty issues, as well ascoping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,only a handful have been considering a Deep Neural Network. Moreover,only one of these studies has addressed the problem of hyper-parameteroptimization, albeit under specific conditions. To contribute to this areaof research, we propose a research design that will investigate a DeepNeural Network with optimized hyper-parameters for its effectiveness toperform link prediction on real-world KGs.