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.

Achieving and maintaining metadata quality: Toward a sustainable workflow for the IDEALS institutional repository

Abstract: This article documents the steps taken to assess metadata errors within the IDEALS repository. It describes the workflows established to create accurate and consistent metadata, focusing especially...

Integrated Metadata Support for Web Service Runtimes

Abstract: Service metadata is an important aspect when developing applications following the service-oriented architecture paradigm. Such metadata includes a description of functionalities offered by a service, pre- and postconditions and data that is produced and consumed by a service, as well as a categorization of functionalities in the domain. Providing expressive metadata for services as part of the runtime infrastructure is necessary to leverage adaptability and autonomic behavior such as dynamic (re-)binding, service selection, invocation and composition. In this paper we present a model and its implementation for adding a reasonable amount of service metadata to foster their use in service-oriented applications and describe how to map concrete Web services to this metadata model. Furthermore, we explain our model based on an illustrative example from the telecommunications domain.

Automatic mining of cognitive metadata using fuzzy inference

Abstract: Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).

A comparison of metadata extraction techniques for crowdsourced bibliographic metadata management

Abstract: Social research networks such as Mendeley and CiteULike offer various services for collaboratively managing bibliographic metadata and uploading textual artifacts. One core problem thereby is the extraction of bibliographic metadata from the textual artifacts. Our work investiages the use of Conditional Random Fields and Support Vector Machines, implemented in two state-of-the-art real-world systems, namely ParsCit and the Mendeley Desktop, for automatically extracting bibliographic metadata. We compare the systems' accuracy on two newly created real-world data sets gathered from Mendeley and Linked-Open-Data repositories. Our analysis shows that two-stage SVMs provide reasonable performance in solving the challenge of metadata extraction from user-provided textual artifacts.

Metadata Clean Sweep: A Digital Library Audit Project

Abstract: As digital library collections grow in size, metadata issues such as inconsistencies, incompleteness and quality become increasingly difficult to manage over time. Unfortunately, successful user search and discoverability of digital collections relies almost entirely on the accuracy and robustness of metadata. This paper discusses the pilot of an ongoing digital library metadata audit project that was collaboratively launched by library school interns and full-time staff to alleviate poor recall, poor precision and metadata inconsistencies across digital collections currently published in the University of Houston Digital Library. Interns and staff designed a multi-step project that included metadata review of sample items from each collection, systematic revision of previously published metadata and recommendations for future metadata procedures and ongoing metadata audit initiatives. No such metadata audit efforts had been conducted on the UH Digital Library and the project yielded data that provided staff with the opportunity to significantly improve the overall quality and consistency of metadata for collections published over the nearly three year life of the repository. This article also contains lessons learned and suggestions on how a similar metadata audit project could be implemented in other libraries hosting digital collections.