Title: Extending the GLOBDEF framework with support for semantic enhancement of various data formats
Authors: Maria Nisheva-Pavlova; Asen Alexandrov
Addresses: Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria ' Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria
Abstract: Semantic enhancement links sections of data files with well-described concepts from some knowledge domain. This allows for further automated reasoning about that data and can be especially useful for extracting value from Big Data. Most of the available enhancement tools focus on specific enhancement needs and data types. In this paper we present our efforts to expand the GLOBDEF framework, introduced in an earlier work, which aims to find a way for processing of large amounts of data and enhancing the data automatically. The framework is designed to leverage a variety of external enhancement tools and has no limitations on the format of the enhanced data. We demonstrate how the framework behaves on a mixed data set of texts and images and explain how an image can be semantically enhanced with a simple automated combination of an object recogniser and a text-based automated enhancer.
Keywords: linked open data; semantic annotation; semantic enhancement; metadata; ontology; unstructured data; automatic annotation; enrichment pipeline; semantic interoperability.
International Journal of Metadata, Semantics and Ontologies, 2020 Vol.14 No.2, pp.158 - 168
Received: 04 Oct 2019
Accepted: 30 Apr 2020
Published online: 02 Jul 2020 *