Meta Data Services

About: Meta Data Services is a research topic. Over the lifetime, 2564 publications have been published within this topic receiving 40102 citations.

Enrichment and Structuring of Archival Description Metadata

Abstract: Cultural heritage institutions are making their digital content available and searchable online. Digital metadata descriptions play an important role in this endeavour. This metadata is mostly manually created and often lacks detailed annotation, consistency and, most importantly, explicit semantic content descriptors which would facilitate online browsing and exploration of available information. This paper proposes the enrichment of existing cultural heritage metadata with automatically generated semantic content descriptors. In particular, it is concerned with metadata encoding archival descriptions (EAD) and proposes to use automatic term recognition and term clustering techniques for knowledge acquisition and content-based document classification purposes.

A critical introduction to metadata for e-science and e-research

Abstract: Scientific research is moving towards multi-disciplinary, multi-institutional collaboration and therefore powerful tools and infrastructures based on interoperability principles are needed to support this trend. This paper introduces the special issue on the metadata for e-science and e-research of the International Journal on Metadata, Semantics and Ontologies. This special issue seeks to draw attention to the on-going challenges that scientists and systems developers face in the area of metadata and data management for e-science and e-research. In particular, the objectives of this special issue are a to present some of the latest research in this field, especially in relation to the use of metadata for addressing challenges associated with the management of scientific and research data across a broad range of applications; and b to highlight some of the challenges associated with the use of metadata, and encourage further research in this area. The special issue includes four papers reporting innovative approaches to key issues in the area of metadata for e-science and e-research, such as metadata modelling and standardisation, data quality and data re-use.

Guidelines for the modelling of statistical data and metadata.

Abstract: The paper consists of five chapters. In chapter 1 a conceptual foundation is proposed, introducing the concepts of statistical metadata, statistical data, and statistical information systems. Chapter 2 discusses which subjects (users and producers of statistical data) and objects (software tools) have needs for statistical metadata, and for which purposes. In chapter 3 the contents of these metadata needs are analysed more systematically, and in more detail; particular emphasis is given to the metadata needs of users of statistical data. Chapter 4 investigates possible sources for the statistical metadata needed. Finally, chapter 5 discusses how the metadata infrastructure of a statistical service could best be organised.

MetaStore: an adaptive metadata management framework for heterogeneous metadata models

Abstract: In this paper, we present MetaStore, a metadata management framework for scientific data repositories. Scientific experiments are generating a deluge of data, and the handling of associated metadata is critical, as it enables discovering, analyzing, reusing, and sharing of scientific data. Moreover, metadata produced by scientific experiments are heterogeneous and subject to frequent changes, demanding a flexible data model. Existing metadata management systems provide a broad range of features for handling scientific metadata. However, the principal limitation of these systems is their architecture design that is restricted towards either a single or at the most a few standard metadata models. Support for handling different types of metadata models, i.e., administrative, descriptive, structural, and provenance metadata, and including community-specific metadata models is not possible with these systems. To address this challenge, we present MetaStore, an adaptive metadata management framework based on a NoSQL database and an RDF triple store. MetaStore provides a set of core functionalities to handle heterogeneous metadata models by automatically generating the necessary software code (services) and on-the-fly extends the functionality of the framework. To handle dynamic metadata and to control metadata quality, MetaStore also provides an extended set of functionalities such as enabling annotation of images and text by integrating the Web Annotation Data Model, allowing communities to define discipline-specific vocabularies using Simple Knowledge Organization System, and providing advanced search and analytical capabilities by integrating the ElasticSearch. To maximize the utilization of the data models supported by NoSQL databases, MetaStore automatically segregates the different categories of metadata in their corresponding data models. Complex provenance graphs and dynamic metadata are modeled and stored in an RDF triple store, whereas the static metadata is stored in a NoSQL database. For enabling large-scale harvesting (sharing) of metadata using the METS standard over the OAI-PMH protocol, MetaStore is designed OAI-compliant. Finally, to show the practical usability of the MetaStore framework and that the requirements from the research communities have been realized, we describe our experience in the adoption of MetaStore for three communities.

Metadata and knowledge management driven web-based learning information system

Abstract: AHKME e-learning system's main aim is to provide a modular and extensible system with adaptive and knowledge management abilities for students and teachers. This system is based on the IMS specifications representing information through metadata, granting semantics to all contents in it and giving them meaning. Metadata is used to satisfy requirements like reusability, interoperability and multipurpose. The system provides authoring tools to define learning methods with adaptive characteristics and tools to create courses allowing users with different roles, promoting several types of collaborative and group learning. It is also endowed with tools to retrieve, import and evaluate learning objects based on metadata, where students can use quality educational contents fitting their characteristics and teachers have the possibility of using quality educational contents to structure their courses. The metadata management and evaluation play an important role in order to get the best results in the teaching/learning process.