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Nanoinformatics: An Emerging Trend in Cancer Therapeutics
Published in Rajesh Singh Tomar, Anurag Jyoti, Shuchi Kaushik, Nanobiotechnology, 2020
Medha Pandya, Snehal Jani, Vishakha Dave, Rakesh Rawal
The NPO [57] was established to characterize nanomaterials involved in cancer research and maintained by the National Centre for Biomedical Ontology. The NPO follows the framework of the basic formal ontology (BFO) and achieved in the ontology web language (OWL), working on well-defined ontology design principles. This ontology represents information concerning preparation, chemical composition, and characterization of nanomaterials reported for different cancers. The NPO provides a common terminology and integration of data with the logical structure of fetching the data from tools and software related to cancer nanomedicine.
A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost
Published in International Journal of Production Research, 2021
Bongjun Ji, Farhad Ameri, Hyunbo Cho
The proposed approach uses a formal ontology for representing a work order and its various attributes. This ontology is referred to as WON throughout this document. WON is an application ontology that imports a top-level ontology (TLO) and a few mid-level ontologies. The objective of WON is to represent various classes and relations related to nonconformity prediction application in the selected industry. The proposed ontology uses BFO as the top-level ontology to support interoperability and ontology reuse. WON also imports a few modules of Industrial Ontology Foundry (IOF) reference ontologies. Therefore, as a BFO-compliant ontology, WON can be aligned with IOF reference ontologies and benefit from the rigorous ontology review processes proposed by IOF which will result in enhanced reusability. Since multiple stakeholders (companies issuing work orders) participate in the use case that motivates this work, data interoperability is a critical factor. Alignment with reference top-level and midlevel ontologies will make the ontology more reusable and understandable by diverse agents.
Interoperable manufacturing knowledge systems
Published in International Journal of Production Research, 2018
Claire Palmer, Zahid Usman, Osiris Canciglieri Junior, Andreia Malucelli, Robert I. M. Young
The aim of manufacturing reference ontologies is to provide an underpinning formal semantic structure that can meet the above need, supporting the development of flexible systems that can share manufacturing knowledge across the multiple manufacturing-related domains. This approach, described in the next sub-section, effectively sit between domain ontologies, which are very specific and foundation ontologies such as DOLCE, SUMO, BFO (Mascardi, Locoro, and Rosso 2010) which are very generic. For example BFO provides formal semantic definitions for terms, such as Continuants, such as Material and Information entities and Occurrents such as Processes. BFO, as other foundation ontologies, leaves the user to specialise these terms into more specific meaningful terms for their own use. In the case of manufacturing, the aim in our work is to exploit this approach to provide the core semantics for manufacturing that enables cross-disciplinary sharing of knowledge i.e. to provide the core semantics for manufacturing that can then be referenced and further specialised to support the multi-context domains needed in a manufacturing organisation. The idea of a reference ontology fits with the integration model concepts of the ‘IIDEAS’ architecture, with foundation concepts that are used to build general concepts that are then used to build specific concepts (West and Fowler 2001), but with the important addition of the use of formal logic.
Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation
Published in Journal of Engineering Design, 2019
Hyunmin Cheong, Adrian Butscher
BFO, or Basic Formal Ontology (Almeida et al. 2015), is an upper ontology of universals with a strict commitment toward ontological realism, as stated earlier. It started as an ontology to represent ‘dynamic features of reality’ (Grenon and Smith 2004) and found significant success in the biomedical domain (Grenon, Smith, and Goldberg 2004), e.g. in the development of Gene Ontology (Ashburner et al. 2000) and the establishment of Open Biomedical Ontologies (OBO) Foundry (Smith et al. 2007). It shares a number of similar categorisations as DOLCE, except a few notable differences such as in material constitution and quality descriptions.