SEMU: A semantic knowledge management system of musical information

The diverse data types of musical information domain including binary and text-based structures create semantic gaps between the entities of different data formats. This leads to difficulties in analyzing, capturing and managing entities of the domain. In this paper, we present a semantic knowledge management system, called SEMU, to efficiently managing musical information. We propose SEMU ontology to capture information extracted from various data types and sources. In order to extract information from raw data, we use Musical Information Retrieval techniques for audio files and Natural Language Processing techniques for text-based formats. We develop a rule-based solution to enrich the system knowledge base. Later, we provide a web application with seamless integration between SEMU knowledge base and user interface to enable users to benefit from the advantages of the SEMU system. (C) 2015 Elsevier B.V. All rights reserved.