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Synthetic Worlds for On-Demand Experience
Published in C.A.P. Smith, Kenneth W. Kisiel, Jeffrey G. Morrison, Working Through Synthetic Worlds, 2009
There are representation techniques such as frames, rules and semantic networks that have originated from theories of human information processing. Over the past several decades, many knowledge representation methods were tried at the problem specific level (e.g., heuristic questions-answering, neural networks, and expert systems) and at the general level (e.g., Cyc, OpenMind). Additionally, several programming languages were developed specifically tailored for knowledge representation and reasoning (e.g., Prolog, KL-ONE). More recently, the emergence of the Semantic Web has seen the development and evolution of representation schemes like XML, RDF, DARPA Agent Markup Language (DAML), Ontology Inference Layer (OIL), and Web Ontology Language (OWL). With all this data being encoded, one might wonder why inferencing is required.
Ontology Modeling
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
The DARPA agent markup language (DAML) and ontology inference layer (OIL) are two different languages, separately developed which later combine to form the DAML+OIL ontology language. The DAML was motivated by the success of the resource description framework (RDF) and extensible markup language, as a result, it became an extension of both languages. The language was aimed to address the lack of expressivity in those early ontology languages. It was a build-up from the DAML-ONT language which was also earlier proposed to support RDF to model complex class definitions. Meanwhile, OIL development was inspired by constructs in XML, RDF, and Open Knowledge Base Connectivity (OKBC). The OIL language was motivated by the need to add expressivity to ontology languages in its building block through advancing their syntax and semantics. It supports the use of constructs that are seen in the objected-oriented paradigm and frame-based ontologies and assumes its semantics from the description logic with the aim of improving automated reasoning on its ontologies. Combining DAML and OIL into DAML+OIL provided ontology engineers with a plethora of language constructs with high ontology expressivity supporting machine understandability. The pooled efforts of DAML and OIL soon became the foundation for the W3C’s proposal for OWL language. Its wide acceptance of expressivity is associated with the use of class, properties, axioms, and definition of complex rules as constructs in the language. The DAML+OIL is limited by the reduction in decidability when the concept of cardinality is added to properties. Some of its shortcomings are now being remedied by OWL.
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).