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Functional Architecture for Knowledge Metadata and Metainformation
Published in Denise Bedford, Knowledge Architectures, 2020
Our traditional metadata architecture consists of (1) record structures or profiles (e.g., predefined sets of field); (2) a metadata registry (e.g., a database structure that houses the records); (3) a repository in some cases of the metadata records and links to the referenced object; (4) in many cases, the larger application that leverages the registry; and (5) references to the controlled reference sources that provide the permissible values for the attributes. In some cases, traditional architecture also includes a mechanism for embedding metadata into assets like properties. In some cases, the metadata architecture is an intentional, stand-alone application that services other applications. It will be an essential transition architecture, but it will not be a complete or sufficient metadata architecture for the future. Consider the size and inelegant nature of a huge metadata repository with a wide range of metadata profiles for different kinds of knowledge assets. Now add in the extended metainformation we need for those knowledge assets, and the additional links to derivative and interpretations. We suddenly have a very complex and difficult way to manipulate a database. Simply turning everything into links is not a practical solution. We need to think about a new design, beginning with objects and attributes. Perhaps the knowledge architecture of the future is grounded in an elegantly designed, more flexible and less rigid architecture.
The Transition to Metadata
Published in Philip J. Cianci, HDTV and the Transition to Digital Broadcasting, 2012
There are a number of metadata registries in existence that facilitate dictionary reference to metadata terminology. The purpose of a metadata registry is to identify the meaning of a term by an application when a particular code point (a unique sequence of bytes that identifies an individual item) is parsed. To do this, the application must know which metadata registry has been used to associate metadata with essence.
Efficient computation of comprehensive statistical information of large OWL datasets: a scalable approach
Published in Enterprise Information Systems, 2023
Heba Mohamed, Said Fathalla, Jens Lehmann, Hajira Jabeen
LODStats (Auer et al. 2012) is an approach, written as a Python module and uses the Redland library (Beckett 2001), for computing 32 different statistical criteria, such as typed string length, max per property, and class hierarchy depth, the results are described using VoID. The main advantage of LODStats, when compared to current approaches, is its significantly better performance and scalability as well as low memory consumption. One of the limitations of LODStat is that it can operate only on a single triple pattern, i.e., it does not support, for example, star patterns (Gottron et al. 2013). Nevertheless, it provides several schema-level statistics, such as RDFS sub-hierarchy depth, and data-level statistics, such as counting triples with literals. LODStats has been integrated with the Comprehensive Knowledge Archive (CKAN)8 dataset metadata registry to get a general overview of the current state of the Data on the Web.
Current status and future directions of geoportals
Published in International Journal of Digital Earth, 2020
Hao Jiang, John van Genderen, Paolo Mazzetti, Hyeongmo Koo, Min Chen
Geoportal common functionalities include a metadata registry, data discovery through a catalogue service, data visualization, and data access. A geospatial metadata catalogue provides data descriptions in terms of metadata (e.g. contributor, data type, language, contact point, keywords, and dataset identifiers for data localization and indexing). In addition, the metadata catalogue is often used for implementing harmonized data discovery. Since users are typically interested in finding datasets matching specific constraints, the data discovery functionality is one of the basic functions that geoportals offer. Specifically, geoportals providing data discovery generally allow searching datasets along the who, when, where and what axes, that is, by geo-location (where), data provider (who), time range (when), thematic layer, and keywords (what). The user interface provides graphical tools, like a bounding box on a map, to set spatial and temporal constraints. Moreover, users can be directed to a gazetteer, a thesaurus, or other knowledge bases for better scoping their query. Various approaches have been developed to enhance geoportal search capabilities, e.g. the use of thesauri, ontologies, and semantic text matching algorithms (Wang, Gong, and Wu 2007; Santoro et al. 2012).