Addiction Ontology: Applying basic formal ontology in the addiction domain

Ontologies are being used in many areas of science to improve clarity and communication of research methods, findings and theories. Many of these ontologies use an upper level ontology called Basic Formal Ontology (BFO) as their frame of reference. This article summarises Basic Formal Ontology and shows how it can provide a basis for development of an Addiction Ontology that encompasses all the things that addiction researchers, practitioners and policy makers want to refer to. BFO makes a fundamental distinction between what it calls continuants (e.g. objects and their characteristics) and occurrents (e.g. processes). Classifying addiction-related entities using this system enables important distinctions to be made that are frequently overlooked or confused in the literature due to inherent ambiguities in natural language expressions. The Addiction Ontology uses this framework to convey information about: people and populations and their characteristics (e.g. substance use disorder), products (e.g. heroin, tobacco-containing products), behaviours (e.g. cigarette smoking, alcohol consumption), interventions (e.g. detoxification, rehabilitation, legislation), research (e.g. measurement, theories, study designs), organisations (e.g. pharmaceutical industry, tobacco companies), and settings (e.g. hospital outpatient clinic, country)

Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes

Classifying entities in domain ontologies under upper ontology classes is a recommended task in ontology engineering to facilitate semantic interoperability and modelling consistency. Integrating upper ontologies this way is difficult and, despite emerging automated methods, remains a largely manual task. Little is known about how well experts perform at upper ontology integration. To develop methodological and tool support, we first need to understand how well experts do this task. We designed a study to measure the performance of human experts at manually classifying classes in a general knowledge domain ontology with entities in the Basic Formal Ontology (BFO), an upper ontology used widely in the biomedical domain. We conclude that manually classifying domain entities under upper ontology classes is indeed very difficult to do correctly. Given the importance of the task and the high degree of inconsistent classifications we encountered, we further conclude that it is necessary to improve the methodological framework surrounding the manual integration of domain and upper ontologies

Drawing Boundaries

In “On Drawing Lines on a Map” (1995), I suggested that the different ways we have of drawing lines on maps open up a new perspective on ontology, resting on a distinction between two sorts of boundaries: fiat and bona fide. “Fiat” means, roughly: human-demarcation-induced. “Bona fide” means, again roughly: a boundary constituted by some real physical discontinuity. I presented a general typology of boundaries based on this opposition and showed how it generates a corresponding typology of the different sorts of objects which boundaries determine or demarcate. In this paper, I describe how the theory of fiat boundaries has evolved since 1995, how it has been applied in areas such as property law and political geography, and how it is being used in contemporary work in formal and applied ontology, especially within the framework of Basic Formal Ontology

Sandra Lapointe (ed.) Themes from Ontology, Mind, and Logic: Present and Past – Essays in Honour of Peter Simons

I review Sandra Lapointe (ed.) "Themes from Ontology, Mind, and Logic: Present and Past – Essays in Honour of Peter Simons"

The Space Object Ontology

Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining longterm space domain awareness

A First-Order Logic Formalization of the Industrial Ontology Foundry Signature Using Basic Formal Ontology

Basic Formal Ontology (BFO) is a top-level ontology used in hundreds of active projects in scientific and other domains. BFO has been selected to serve as top-level ontology in the Industrial Ontologies Foundry (IOF), an initiative to create a suite of ontologies to support digital manufacturing on the part of representatives from a number of branches of the advanced manufacturing industries. We here present a first draft set of axioms and definitions of an IOF upper ontology descending from BFO. The axiomatization is designed to capture the meanings of terms commonly used in manufacturing and is designed to serve as starting point for the construction of the IOF ontology suite

An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart