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Data Collection and Representation
Published in Brecht De Man, Ryan Stables, Joshua D. Reiss, IntelligentMusic Production, 2019
Brecht De Man, Ryan Stables, Joshua D. Reiss
An ontology is a semantic data model, in which domain knowledge is formalized, to provide a rigorous underlying set of relationships between objects [203]. Ontologies are at the core of a wide range of technologies behind the semantic web, as they aim to quantify knowledge and represent it in an extensible, homogeneous format. Ontology languages such as OWL (Web Ontology Language) [204] and frameworks like RDF enable ontological concepts to be encoded, which in turn are stored in SPARQL-based data stores. These form the infrastructure for a network of interlinked uniform resource identifiers (URIs), in which knowledge can be formalized, distributed and reasoned upon by computational agents. In audio engineering a number of key ontologies are relevant to the representation of production data, some of which are being used extensively for the storage, representation and processing of digital media.
Data Science with Semantic Technologies: Application to Information Systems Development
Published in Journal of Computer Information Systems, 2023
Semantic technologies are used with data science with the ultimate goal to make sense to data, by making it interpretable and meaningful.55 They may intervene either upstream, i.e. in first steps of the data science process for retrieving or preparing data for example, or at the end of the process when it is a question of interpreting the results or recommending best actions that is downstream. Semantics imply the study of representation, sharing, and processing of meaning in computer systems.83,84 Gorodetsky reports among semantic technologies the following: ontologies and semantic models of their use, semantic resources, and the semantic component of the technology.6 As to Bandara and Rabhi, they cite domain knowledge, analytics knowledge, services, and user intentions.7 Hence, we can obviously see that to integrate semantics with data science, we may rely on the three main sources of semantic knowledge presented in “Semantic technologies components” section: Semantic data model refers to any conceptual data model that includes semantic information in the purpose of adding a basic meaning to the data and the relationships that lie between them. The semantics of a semantic data model stem from the intentional declarations of objects names, values, and relationship sets and declared constraints that the data should satisfy.85 Under the umbrella of semantic data model, we may classify all data representations that can handle semantics.Semantic resources are one of the basic pillars enabling to determine semantics of text entities and disambiguate their meaning. They represent the source knowledge about natural language semantics serving as basis to understand means of words through some process of similarity measures computing. These resources tend to be universal and publicly available.Semantic component of technology refers to any process or computer program (e.g., related to artificial intelligence, natural language, software engineering, data and knowledge engineering, computer systems, signal processing, etc.) that aims at or used to understand the meaning of computational content.