Abstract:In this paper we investigate many of the various storage, portability and interoperability issues arising among archaeologists and cultural heritage people when dealing with 3D technologies. On the one side, the available digital repositories look often unable to guarantee affordable features in the management of 3D models and their metadata; on the other side the nature of most of the available data format for 3D encoding seem to be not satisfactory for the necessary portability required nowadays by 3D information across different systems. We propose a set of possible solutions to show how integration can be achieved through the use of well known and wide accepted standards for data encoding and data storage. Using a set of 3D models acquired during various archaeological campaigns and a number of open source tools, we have implemented a straightforward encoding process to generate meaningful semantic data and metadata. We will also present the interoperability process carried out to integrate the encoded 3D models and the geographic features produced by the archaeologists. Finally we will report the preliminary (rather encouraging) development of a semantic enabled and persistent digital repository, where 3D models (but also any kind of digital data and metadata) can easily be stored, retrieved and shared with the content of other digital archives.
Metadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library “Cultura Italia” and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 $$\sim $$ ∼ 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance.
Metadata allows access to a wide variety of cultural heritage resources made available through repositories, digital libraries, and catalogues. Usually taking the form of a structured set of descriptive elements, metadata assist in the identification, location, processing, tracking, preserving, sharing, and retrieval of information, while facilitating content and access management. However, low metadata quality, such as the lack of mandatory information, incorrect information, or inconsistency, is still an open issue in many repositories. In this article, we present our ongoing work aiming at automatizing the metadata quality analysis, and the preliminary results on metadata completeness for the Italian digital library ‘Cultura Italia’.
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