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Analysis of Ontology-Based Semantic Association Rule Mining
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Ontologies are suitable for semantic association mining as they have a semantically rich taxonomic structure that consists of concepts and their relations. They are described by the ontology specification languages such as RDFS, OWL, OIL, and DAML + OIL. OIL (Ontology Interchange Language) is used to combine frame-based model primitives with semantics and reasoning services provided by description logic. DAML (DARPA Agent Markup Language) is a semantic markup language specifically designed for the web which is based on RDF and DAML + OIL. Ontologies help to generalize the relations identified by the semantic pattern mining algorithm and improve the quality by removing the redundant patterns [9]. Hence, ontologies are adapted in semantic association mining algorithms to extract the deep semantic association patterns and to reduce the number of shallow associations. The recommendation system for metadata, ranking of semantic medical association rules, and information extraction system is the few real-time applications of the ontology-based semantic association rule mining approach [10–12].
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Published in International Journal of Production Research, 2021
Chandan K. Sahu, Crystal Young, Rahul Rai
An ontology is an AI-based storage method that is able to not only store data in a logical and organised manner but is able to derive relationships between data instances. Ontologies are data storage mechanisms that are a part of the Semantic Web (Crowder et al. 2009) and can be defined as a specification of a conceptualisation (Gruber 1993). One of the benefits of using an ontology to describe domain knowledge as opposed to a database is that semantic queries can be completed in ontologies using SPARQL Protocol and Resource Description Framework (RDF) Query Language (SPARQL) (Zhao and Qian 2017). Ontologies are composed of concepts or classes and instances or individuals where concepts describe categories within a domain, and instances are specifications of these categories or concepts. Ontologies that have instances or individuals defined are deemed knowledge bases (Noy and McGuinness 2000). Many file formats have been created to store ontologies, including RDF, web ontology language (OWL) and its variants, and United States Defense Advanced Research Project Agency (DARPA) agent markup language and the ontology inference layer (DAML + OIL) (Munir and Anjum 2018). The main advantage of using an ontology to store data is the ability to use an ontological or semantic reasoner such as Jena, HermiT, ELK, or Pellet. These reasoners are able to infer logical consequences between ontology instances based on asserted facts or axioms (Abburu 2012). An additional advantage of using an ontology is the ability to integrate various sources of data throughout the PLM (Ali et al. 2019).
Agent-based distributed manufacturing scheduling: an ontological approach
Published in Cogent Engineering, 2019
Salman Saeidlou, Mozafar Saadat, Ebrahim Amini Sharifi, Guiovanni D. Jules
For the scope of this research, a manufacturing ontology is built with Protégé ontology editor software, which is a free and open-source leading ontological engineering tool. Protégé has relevant advantages over the other platforms. It provides a graphical user interface to develop the ontology that requires minimum expertie and time to be built. It provides an interface with other knowledge-based tools such as Java Expert System Shell (JESS) and is compatible with various ontology languages and formats such as eXtensible Markup Language (XML), DARPA Agent Markup Language (DAML) and Ontology Inference Layer (OIL) (Gaševic, Djuric, & Devedžic, 2009). In addition, it is particularly suitable for this research as it has deductive classifiers for validation of the ontology consistency. Furthermore, it can also export into other formats such as RDF, which is the basis of this research. When modelling in a domain, developers must be able to focus more on the concepts and relations rather than the syntax of the final results. This can be achieved through a Protégé-based editor, resulting in modelling at a conceptual level (Gennari et al., 2003; Noy et al., 2001).