Microsoft Cloud-based Digitization Workflow with Rich Metadata Acquisition for Cultural Heritage Objects

• URL: http://arxiv.org/abs/2407.06972v1
• Date: Tue, 9 Jul 2024 15:49:47 GMT
• Title: Microsoft Cloud-based Digitization Workflow with Rich Metadata Acquisition for Cultural Heritage Objects
• Authors: Krzysztof Kutt, Jakub Gomułka, Luiz do Valle Miranda, Grzegorz J. Nalepa,
• Abstract summary: We have developed a new digitization workflow with the Jagiellonian Library (JL) The solution is based on easy-to-access technological solutions -- Microsoft cloud with MS Excel files interfaces, Office Script for metadata acquisition, MS 365 for storage -- that allows metadata acquisition by domain experts. The ultimate goal is to create a knowledge graph that describes the analyzed holdings, linked to general knowledge bases, as well as to other cultural heritage collections.
• Score: 7.450700594277742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In response to several cultural heritage initiatives at the Jagiellonian University, we have developed a new digitization workflow in collaboration with the Jagiellonian Library (JL). The solution is based on easy-to-access technological solutions -- Microsoft 365 cloud with MS Excel files as metadata acquisition interfaces, Office Script for validation, and MS Sharepoint for storage -- that allows metadata acquisition by domain experts (philologists, historians, philosophers, librarians, archivists, curators, etc.) regardless of their experience with information systems. The ultimate goal is to create a knowledge graph that describes the analyzed holdings, linked to general knowledge bases, as well as to other cultural heritage collections, so careful attention is paid to the high accuracy of metadata and proper links to external sources. The workflow has already been evaluated in two pilots in the DiHeLib project focused on digitizing the so-called "Berlin Collection" and in two workshops with international guests, which allowed for its refinement and confirmation of its correctness and usability for JL. As the proposed workflow does not interfere with existing systems or domain guidelines regarding digitization and basic metadata collection in a given institution (e.g., file type, image quality, use of Dublin Core/MARC-21), but extends them in order to enable rich metadata collection, not previously possible, we believe that it could be of interest to all GLAMs (galleries, libraries, archives, and museums).

Related papers

• Extending and Applying Automated HERMES Software Publication Workflows [0.6157382820537718]
  HERMES is a tool that automates the publication of software with rich metadata. We show how to use HERMES as an end user, both via the informal command line interface, and as a step in a continuous integration pipeline.
  arXiv  Detail & Related papers  (2024-10-23T07:11:48Z)
• DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models [66.91204604417912]
  This study aims to enhance generalizability of small VDU models by distilling knowledge from LLMs. We present a new framework (called DocKD) that enriches the data generation process by integrating external document knowledge. Experiments show that DocKD produces high-quality document annotations and surpasses the direct knowledge distillation approach.
  arXiv  Detail & Related papers  (2024-10-04T00:53:32Z)
• Advancing Manuscript Metadata: Work in Progress at the Jagiellonian University [7.993453987882035]
  Three Jagiellonian University units are collaborating to digitize cultural heritage documents, describe them in detail, and then integrate these descriptions into a linked data cloud. We present a report on the current status of the work, in which we outline the most important requirements for the data model under development. We make a detailed comparison with the two standards that are the most relevant from the point of view of collections: Europeana Data Model used in Europeana and Encoded Archival Description used in Kalliope.
  arXiv  Detail & Related papers  (2024-07-09T15:52:06Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models [11.959445364035734]
  80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats. We introduce Docs2KG, a novel framework designed to extract multimodal information from diverse and heterogeneous documents. Docs2KG generates a unified knowledge graph that represents the extracted key information.
  arXiv  Detail & Related papers  (2024-06-05T05:35:59Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• Learning to Learn from APIs: Black-Box Data-Free Meta-Learning [95.41441357931397]
  Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing DFML work can only meta-learn from (i) white-box and (ii) small-scale pre-trained models. We propose a Bi-level Data-free Meta Knowledge Distillation (BiDf-MKD) framework to transfer more general meta knowledge from a collection of black-box APIs to one single model.
  arXiv  Detail & Related papers  (2023-05-28T18:00:12Z)
• LAVIS: A Library for Language-Vision Intelligence [98.88477610704938]
  LAVIS is an open-source library for LAnguage-VISion research and applications. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets.
  arXiv  Detail & Related papers  (2022-09-15T18:04:10Z)
• PyRelationAL: A Library for Active Learning Research and Development [0.11545092788508224]
  PyRelationAL is an open source library for active learning (AL) research. It provides access to benchmark datasets and AL task configurations based on existing literature. We perform experiments on the PyRelationAL collection of benchmark datasets and showcase the considerable economies that AL can provide.
  arXiv  Detail & Related papers  (2022-05-23T08:21:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.