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Background The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. Methods An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. Results We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. Conclusions ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.

As engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasonin...

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).

Fundamentally, the engineering designs are collections of information, which contain the structural, qualitative and the behavioral (kinematic and static) specifications of some physical products that satisfy some customer requirements. Vast suits of information exchange standards, most notably ISO10303 (STEP), are currently being used for capturing product design in manufacturing industries. In recent years, a modern practice of capturing the product design information using model-based definition (MBD) (STEP AP242, ASME Y14.5) is becoming increasingly popular due to its ability to capture geometrical dimension and tolerances (GD&T) against managed models of 3D design features instead of traditional 2D drafting, facilitating communication of the design intents, and product model information (PMI). However, MBD suffers from difficulties in standardization of managed models due to diversity of views, stemming from cultural and traditional differences among the product designers and organizations. In the wake of semantic web technologies, researchers proposed number of semantic models for capturing product design concepts based on ontological taxonomy and rules, aiming to integrate heterogeneous sources of data based on contextual meaning of the information. In this article, we present an upper level ontology for capturing MBD based product designs, including axiomatic definitions of concepts related to product design (e.g. part, topology, feature, dimension, and tolerance). The primary goal of such semantic model is to increase the interoperability of MBD based product design and integrate domain-specific design features from manufacturability perspective (CNC, sheet metal, molding, 3D printing). In order to achieve such cross-domain interoperability, we based our design on Basic Formal Ontology (BFO). The applicability of the proposed ontology is elucidated by presenting usage pattern based on example design.

The promise of distributed cloud manufacturing (CM) is to manufacture products using shared resources, both asset-full (machines, tools, vehicles) and asset-light (design, analysis, inspection, management, maintenance), which can be provisioned flexibly and rapidly with minimal management and service provider interaction. One of the key enabler of CM are virtual enterprises (VE), which offer manufacturing resources as virtual services (SaaS, HaaS, PaaS) in a cloud based marketplace. Currently, virtualization and provisioning of diverse array of manufacturing resources face challenges from the heterogeneity in representation and communication protocols, as well as lack of integration with legacy practices in the organizations. Aiming to increase interoperability, a number of formal ontologies were developed by researchers in the past, to leverage on semantic data integration and validation. In spite of their success in providing axiomatic description and common taxonomy to classify manufacturing resources from different domains, models of representing the capabilities of the resources (i.e. expected quality of services they offer) were often overlooked. In this research, we present an ontology model to represent capabilities of manufacturing machine-tools at the process level, often called process boundaries (measured by process capability index) in industries. The definition of the capability is derived based on the foundational ontology called ‘Basic Formal Ontology’ (BFO). The primary contribution of this extension is a set of OWL axioms which can be used to assert facts about modal future (possibilia) – ultimately enabling us to associate process specific performance metrics to the semantic models of virtual machine-tools.

A conceptual model abstractly defines a number of concepts and their relationships for the purposes of understanding and communication. Once a conceptual model is available, it can also be used as a starting point for the development of a software system. The development of conceptual models using the Unified Modeling Language (UML) facilitates the representation of modeled concepts and allows software developers to directly reuse these concepts in the design of a software system. The OBO Foundry represents the most relevant collaborative effort towards the development of ontologies in the biomedical domain. The development of UML conceptual models in the biomedical domain may benefit from the use of domain-specific semantics and notation. Further, the development of these models may also benefit from the reuse of knowledge contained in OBO ontologies. This paper investigates the support for the development of conceptual models in the biomedical domain using UML as a conceptual modeling language and using the support provided by the OBO Foundry for the development of biomedical ontologies, namely entity kind and relationship types definitions provided by the Basic Formal Ontology (BFO) and the OBO Core Relations Ontology (OBO Core), respectively. Further, the paper investigates the support for the reuse of biomedical knowledge currently available in OBOFFF ontologies in the development these conceptual models. The paper describes a UML profile for the OBO Core Relations Ontology, which basically defines a number of stereotypes to represent BFO entity kinds and OBO Core relationship types definitions. The paper also presents a support toolset consisting of a graphical editor named OBO-RO Editor, which directly supports the development of UML models using the extensions defined by our profile, and a command-line tool named OBO2UML, which directly converts an OBOFFF ontology into a UML model.

Background Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. Results This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients’ current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology’s BioPortal at http://bioportal.bioontology.org/ontologies/DMTO. The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. Conclusion The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems. Electronic supplementary material The online version of this article (10.1186/s13326-018-0176-y) contains supplementary material, which is available to authorized users.

Background Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs. Methods This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology. Results This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients’ wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO. Conclusions The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies. Electronic supplementary material The online version of this article (10.1186/s12911-019-0806-z) contains supplementary material, which is available to authorized users.

Background With the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a consequence, there is an increasing need for an integrated system of hierarchies of levels of different types of material entities that helps with organizing, structuring and integrating data from disparate sources to facilitate data exploration, data comparison and analysis. Theories of granularity provide such integrated systems. Results On the basis of formal approaches to theories of granularity authored by information scientists and ontology researchers, I discuss the shortcomings of some applications of the concept of levels and argue that the general theory of granularity proposed by Keet circumvents these problems. I introduce the concept of building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet’s theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a domain granularity framework for the life sciences. I also discuss the consequences of this granularity framework for the structure of the top-level category of ‘material entity’ in Basic Formal Ontology. Conclusions The domain granularity framework suggested here is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed, which would provide the much needed conceptual framework for representing domains that cover multiple granularity levels. This framework can be used for intuitively structuring data in the life sciences, facilitating data exploration, and it can be employed for reasoning over different granularity levels across different hierarchies. It would provide a methodological basis for establishing comparability between data sets and for quantitatively measuring their degree of semantic similarity.

In the context of globalization and knowledge management, information technologies require an ample need of unprecedented levels of data exchange and sharing to allow collaboration between heterogeneous systems. Yet, understanding the semantics of the exchanged data is one of the major challenges. Semantic interoperability can be ensured by capturing knowledge from diverse sources by using ontologies and align these latter by using upper-level ontologies to come up with a common shared vocabulary. In this paper, we aim in one hand to investigate the role of upper-level ontologies as a mean for enabling the formalization and integration of heterogeneous sources of information and how it may support interoperability of systems. On the other hand, we present several upper-level ontologies and how we chose and then used basic formal ontology (BFO) as an upper-level ontology and common core ontology (CCO) as a mid-level ontology to develop a modular ontology that defines emergency responders’ knowledge starting from firefighters’ module for a solution to the semantic interoperability problem in emergency management.

This chapter proposes Plateforme OperationnelLe d'Actualisation du Renseignement Interservices pour la Securite Civile (POLARISC), an ontology‐based operational emergency response (ER) system for smart cities. In addition, POLARISC offers a set of services for smart cities including alerting smart actors, the evacuation of victims, providing Emergency Detour Route and so on. The chapter illustrates the architecture of POLARISC as a whole system. POLARISC is mainly a software solution that plays the role of mediation between ERs. The modular ontology is the first ontology based on the Basic Formal Ontology (BFO) and Common Core Ontology (CCO) that aims to define the ERs knowledge. Once POLARISC ontology is done and the common operating platform is established, the cited smart cities services can be enabled.

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

Background. 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. Problem. 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.Method. We recruited 8 BFO experts and asked them to classify 46 commonly known entities from the domain of travel with BFO entities. The tasks were delivered as part of a web survey.Results. We find that, even for a well understood general knowledge domain such as travel, the results of the manual classification tasks are highly inconsistent: the mean agreement of the participants with the classification decisions of an expert panel was only 51%, and the inter-rater agreement using Fleiss’ Kappa was merely moderate (0.52). We further follow up on the conjecture that the degree of classification consistency is correlated with the frequency the respective BFO classes are used in practice and find that this is only true to a moderate degree (0.52, Pearson).Conclusions. 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.

Graphical abstractDisplay Omitted TRAK ontology models domain-specific knowledge about the rehabilitation of knee conditions.TRAK provides terminology, definitions and graphical illustrations of knee treatments.TRAK supports shared understanding of standard care in knee rehabilitation.TRAK provides the framework that can be used to collect data for epidemiologic studies of knee conditions.TRAK can support randomized control with a control intervention that is well defined and reflects clinical practice. In this paper we discuss the design and development of TRAK (Taxonomy for RehAbilitation of Knee conditions), an ontology that formally models information relevant for the rehabilitation of knee conditions. TRAK provides the framework that can be used to collect coded data in sufficient detail to support epidemiologic studies so that the most effective treatment components can be identified, new interventions developed and the quality of future randomized control trials improved to incorporate a control intervention that is well defined and reflects clinical practice. TRAK follows design principles recommended by the Open Biomedical Ontologies (OBO) Foundry. TRAK uses the Basic Formal Ontology (BFO) as the upper-level ontology and refers to other relevant ontologies such as Information Artifact Ontology (IAO), Ontology for General Medical Science (OGMS) and Phenotype And Trait Ontology (PATO). TRAK is orthogonal to other bio-ontologies and represents domain-specific knowledge about treatments and modalities used in rehabilitation of knee conditions. Definitions of typical exercises used as treatment modalities are supported with appropriate illustrations, which can be viewed in the OBO-Edit ontology editor. The vast majority of other classes in TRAK are cross-referenced to the Unified Medical Language System (UMLS) to facilitate future integration with other terminological sources. TRAK is implemented in OBO, a format widely used by the OBO community. TRAK is available for download from http://www.cs.cf.ac.uk/trak. In addition, its public release can be accessed through BioPortal, where it can be browsed, searched and visualized.

Today, Complex African Society; African Traditional Medicine ATM is used in parallel to Conventional Medicine CM. Various attempts have been made to bridge the gap between African Traditional Medicine and Conventional Medicine in order to harmonize treatment and to create equal form of therapeutic cooperation. Although there is some minimal form of collaboration, African Traditional Medicine lacks formal structures; each practitioner has his/her own ways of providing healing services. Knowledge generated from Conventional Medicine is structured unlike African Traditional Medicine whose knowledge is not structured, isolated and mistrusted yet a lot of knowledge is generated in the practices which can be use across whole health sector. In this paper we are proposing ontology as a model to bridge the gap between African Traditional Medicine and Conventional Medicine for the benefit of users. We are proposing a methodology for structuring ATM knowledge as an extension of Basic Formal Ontology BFO; a standard upper level ontology.

Pre-eclampsia (PE) is a clinical syndrome characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation, and is a leading cause of maternal and perinatal morbidity and mortality. Previous studies have gathered abundant data about PE such as risk factors and pathological findings. However, most of these data are not semantically structured. Clinical data on PE patients are often generated with semantic heterogeneity such as using disparate terminology to describe the same phenomena. In clinical studies, interoperability of heterogenic clinical data is required in various situations. In such a situation, it is necessary to develop an interoperable and standardized semantic framework to research the pathology of PE more comprehensively and to achieve interoperability of heterogenic clinical data of PE patients. In this study, we developed an ontology representing clinical features, treatments, genetic factors, environmental factors, and other aspects of the current knowledge in the domain of PE. We call this pre-eclampsia ontology "PEO". To achieve interoperability with other ontologies, the core structure of PEO was compliant with the hierarchy of the Basic Formal Ontology (BFO). The PEO incorporates a wide range of key concepts and terms of PE from clinical and biomedical research in structuring the knowledge base that is specific to PE; therefore, PEO is expected to enhance PE-specific information retrieval and knowledge discovery in both clinical and biomedical research fields.

Diabetes mellitus is a major cause of morbidity and mortality in humans. Early diagnosis is the first step toward the management of this condition. However, a diagnosis involves several variables, which makes it difficult to arrive at an accurate and timely diagnosis and to construct accurate personalized treatment plans. An electronic health record system requires an integrated decision support capability, and ontologies are rapidly becoming necessary for the design of efficient, reliable, extendable, reusable, and semantically intelligent knowledge bases. In this study, we take the first step in this direction, by designing an OWL2 diabetes diagnosis ontology (DDO). Protege 5 software was used for the construction of the ontology. DDO is developed within the framework of the basic formal ontology and the ontology for general medical science to represent entities in the domain of diabetes, and it follows the design principles recommended by the Open Biomedical Ontology Foundry. Currently, DDO contains 6444 concepts, 48 properties, 13,551 annotations, and 27,127 axioms. DDO can serve as a diabetes knowledge base and supports automatic reasoning. It represents a major step toward the development of a new generation of patient-centric decision support tools. DDO is available through BioPortal at: http://www.bioportal.bioontology.org/ontologies/DDO .

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl. This work was supported by the National Institute of Allergy and Infectious Diseases (https:// www.niaid.nih.gov/Pages/default.aspx) R01AI081062 to YH, the National Institutes of Health (http://www. nih.gov/) R01GM093132, U01 DK 072473, P41 HG003619 and HHSN272201400030C to CJS, HHSN272201400028C to RHS, HHSN272201200010C and 1U19AI118626 to BP, the California Institute for Regenerative Medicine (https:// www.cirm.ca.gov) GC1R-06673-B to RHS, the National Science Foundation Division of Biological Infrastructure (http://www.nsf.gov) 1458400 to MCC

Background There have been relatively few attempts to represent vision or blindness ontologically. This is unsurprising as the related phenomena of sight and blindness are difficult to represent ontologically for a variety of reasons. Blindness has escaped ontological capture at least in part because: blindness or the employment of the term ‘blindness’ seems to vary from context to context, blindness can present in a myriad of types and degrees, and there is no precedent for representing complex phenomena such as blindness. Methods We explore current attempts to represent vision or blindness, and show how these attempts fail at representing subtypes of blindness (viz., color blindness, flash blindness, and inattentional blindness). We examine the results found through a review of current attempts and identify where they have failed. Results By analyzing our test cases of different types of blindness along with the strengths and weaknesses of previous attempts, we have identified the general features of blindness and vision. We propose an ontological solution to represent vision and blindness, which capitalizes on resources afforded to one who utilizes the Basic Formal Ontology as an upper-level ontology. Conclusions The solution we propose here involves specifying the trigger conditions of a disposition as well as the processes that realize that disposition. Once these are specified we can characterize vision as a function that is realized by certain (in this case) biological processes under a range of triggering conditions. When the range of conditions under which the processes can be realized are reduced beyond a certain threshold, we are able to say that blindness is present. We characterize vision as a function that is realized as a seeing process and blindness as a reduction in the conditions under which the sight function is realized. This solution is desirable because it leverages current features of a major upper-level ontology, accurately captures the phenomenon of blindness, and can be implemented in many domain-specific ontologies.

In information systems, ontologies promise advantages such as enhanced interoperability, knowledge sharing, and integration of data sources. In this article, we show that the upper ontology Basic Formal Ontology can facilitate the modeling of an evolution of administrative units. This is demonstrated by creating a spatiotemporal ontology for the administrative units of Switzerland. The ontology tackles the problem that the geometric data is typically captured by taking snapshots at regular intervals while the thematic data is continually updated. The ontology presented merges time-stamped geometries with a formally described history of administrative units, allowing for complex spatiotemporal queries neither standard approach would support. The resulting populated knowledge base was evaluated against a set of spatiotemporal test queries. The evaluation showed that this knowledge base supports a wide range of queries on the evolution of the administrative units of Switzerland between 1960 and 2010.

Data generated from cancer nanotechnology research are so diverse and large in volume that it is difficult to share and efficiently use them without informatics tools. In particular, ontologies that provide a unifying knowledge framework for annotating the data are required to facilitate the semantic integration, knowledge-based searching, unambiguous interpretation, mining and inferencing of the data using informatics methods. In this paper, we discuss the design and development of NanoParticle Ontology (NPO), which is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO.

Purpose A substantial fraction of the observations made by clinicians and entered into patient records are expressed by means of negation or by using terms which contain negative qualifiers (as in "absence of pulse" or "surgical procedure not performed"). This seems at first sight to present problems for ontologies, terminologies and data repositories that adhere to a realist view and thus reject any reference to putative non-existing entities. Basic Formal Ontology (BFO) and Referent Tracking (RT) are examples of such paradigms. The purpose of the research here described was to test a proposal to capture negative findings in electronic health record systems based on BFO and RT. Methods We analysed a series of negative findings encountered in 748 sentences taken from 41 patient charts. We classified the phenomena described in terms of the various top-level categories and relations defined in BFO, taking into account the role of negation in the corresponding descriptions. We also studied terms from SNOMED-CT containing one or other form of negation. We then explored ways to represent the described phenomena by means of the types of representational units available to realist ontologies such as BFO. Results We introduced a new family of ‘lacks' relations into the OBO Relation Ontology. The relation lacks_part , for example, defined in terms of the positive relation part_of , holds between a particular p and a universal u when p has no instance of u as part. Since p and u both exist, assertions involving ‘ lacks_part ' and its cognates meet the requirements of positivity. Conclusion By expanding the OBO Relation Ontology, we were able to accommodate nearly all occurrences of negative findings in the sample studied.

The central hypothesis underlying this communication is that the methodology and conceptual rigor of a philosophically inspired formal ontology can bring significant benefits in the development and maintenance of application ontologies [A. Flett, M. Dos Santos, W. Ceusters, Some Ontology Engineering Procedures and their Supporting Technologies, EKAW2002, 2003]. This hypothesis has been tested in the collaboration between Language and Computing (L&C), a company specializing in software for supporting natural language processing especially in the medical field, and the Institute for Formal Ontology and Medical Information Science (IFOMIS), an academic research institution concerned with the theoretical foundations of ontology. In the course of this collaboration L&C's ontology, LinKBase, which is designed to integrate and support reasoning across a plurality of external databases, has been subjected to a thorough auditing on the basis of the principles underlying IFOMIS's Basic Formal Ontology (BFO) [B. Smith, Basic Formal Ontology, 2002. http://ontology.buffalo.edu/bfo]. The goal is to transform a large terminology-based ontology into one with the ability to support reasoning applications. Our general procedure has been the implementation of a meta-ontological definition space in which the definitions of all the concepts and relations in LinKBase are standardized in the framework of first-order logic. In this paper we describe how this principles-based standardization has led to a greater degree of internal coherence of the LinKBase structure, and how it has facilitated the construction of mappings between external databases using LinKBase as translation hub. We argue that the collaboration here described represents a new phase in the quest to solve the so-called "Tower of Babel" problem of ontology integration [F. Montayne, J. Flanagan, Formal Ontology: The Foundation for Natural Language Processing, 2003. http://www.landcglobal.com/].

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance.Objectives: TRANSFoRm’s general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care.Methods: TRANSFoRm utilizes a unified structural / terminological interoperability frame work, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care.Results: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm’s use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM.Conclusion: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

The IPAP Schizophrenia Algorithm was originally designed in the form of a flow-chart to help physicians optimise the treatment of schizophrenic patients in the spirit of guideline-based medicine. We take this algorithm as our starting point in investigating how artifacts of this sort can benefit from the facilities of high-quality ontologies. The IPAP algorithm exists thus far only in a form suitable for use by human beings. We draw on the resources of Basic Formal Ontology (BFO) in order to show how such an algorithm can be enhanced in such a way that it can be used in Semantic Web and related applications. We found that BFO provides a framework that is able to capture in a rigorous way all the types of entities represented in the IPAP Schizophrenia Algorithm in way which yields a computational tool that can be used by software agents to perform monitoring and control of schizophrenic patients. We discuss the issues involved in building an application ontology for this purpose, issues which are important for any Semantic Web application in the life science and healthcare domains.

Background Child injury has been a significant societal problem in many countries and various data-mining methodologies have been employed to extract useful information from various injury data sources. However differences of frameworks and vocabularies used by these different sources hinder the integrated analysis across the different source data. The study aims to develop a conceptual child injury data model, Child Injury Information Ontology (CIIO), and to apply this model to Kids Design Database (KDDB) for capturing useful information for various stakeholders relating to product safety issues. Methods Firstly, Kids Injury Information Description Framework (KIIDF), a rearranged and non-duplicating set of all attributes of KDDB’s four different databases, is presented. Secondly, an ontology of KIIDF is created using Basic Formal Ontology (BFO) methodology and Ontologies Inverse Engineering (OIE) technique. An ontology editor named “Hozo” is employed for this purpose. Results Conceptual model of kids injury information with fifty conceptual-level attributes, which are organised into five conceptual categories (host, vector, agent, environment and consequences), and a series of vocabulary standards for the description of those attributes have been produced. Thus, an adequate ontology for child injury information has been obtained. Usefulness of CIIO is presented through the comparative analysis of two product domains, products designed for children and products designed for adults. The conventional analysis indicates that the latter is safer, but not actually safer when CIIO based analysis were made. In order to provide effective protection for children, more care needs to be taken in the design of products for adults. Conclusions CIIO is a powerful tool for the integration and cross-referencing of various types of child injury information. CIIO would be useful for various data mining works for injury prevention at both social and technical levels.

Background: Working interoperability can only be demonstrated with running systems. A prerequisite is an installation and setup of the appropriate interfaces. Currently, this process is performed manually. Objectives: This paper aims at analyzing the background to migrate that process to an automated installation. Methods: The solution is based on the Generic Component Model (GCM) as an architectural framework and the Communication Standards Ontology (CSO) and Basic Formal Ontology (BFO) presented in previous papers. Results: An ontology-based approach to automatically design communication interfaces is proposed. Conclusions: The offered approach can be extended to Webservice adaptations, structural translation of documents or terminology services.

Summary Background: Part of the ReMINE project involved the creation of an ontology enabling computer-assisted decision support for optimal adverse event management. Objectives: The ontology was required to satisfy the following requirements: 1) to be able to account for the distinct and context-dependent ways in which authoritative sources define the term ‘adverse event’, 2) to allow the identification of relevant risks against patient safety (RAPS) on the basis of the disease history of a patient as documented in electronic health records, and 3) to be compatible with present and future ontologies developed under the Open Biomedical Ontology (OBO) Foundry framework. Methods: We used as feeder ontologies the Basic Formal Ontology, the Foundational Model of Anatomy, the Ontology for General Medical Science, the Information Artifact Ontology and the Ontology of Mental Health. We further used relations defined according to the pattern set forth in the OBO Relation Ontology. In light of the intended use of the ontology for the representation of adverse events that have actually occurred and therefore are registered in a database, we also applied the principles of referent tracking. Results: We merged the upper portions of the mentioned feeder ontologies and introduced 22 additional representational units of which 13 are generally applicable in biomedicine and nine in the adverse event context. We provided for each representational unit a textual definition that can be translated into equivalent formal definitions. Conclusion: The resulting ontology satisfies all of the requirements set forth. Merging the feeder ontologies, although all designed under the OBO Foundry principles, brought new insight into what the representational units of such ontologies actually denote.

Definitive diagnosis of malaria requires the demonstration through laboratory tests of the presence within the patient of malaria parasites or their components. Since malaria parasites can be present even in the absence of malaria manifestations, and since symptoms of malaria can be manifested even in the absence of malaria parasites, malaria diagnosis raises important issues for the adequate understanding of disease, etiology and diagnosis. One approach to the resolution of these issues adopts a realist view, according to which the needed clarifications will be derived from a careful representation of the entities on the side of the patient which form the ultimate truthmakers for clinical statements. We here address a challenge to this realist approach relating to the diagnosis of malaria, and show how this challenge can be resolved by appeal to Basic Formal Ontology (BFO) and to the Ontology for General Medical Science (OGMS) constructed in its terms.

The syntax of LBFO represents the initial step toward the creation of a rigorously characterized, recursively defined, artificial language for the sole purpose of ontology development. The underlying idea is that maximally fruitful application of ontology requires accurate representation of reality in accordance with current textbook science. Hence, creating a robust, accurate representation of reality is a fundamental concern. An ontology represents general types of entities and relations between them. A domain ontology represents the general types and relations for a given domain of research. A top-level ontology represents the general types of entities in any domain of research. Ontologies serve many purposes in computerized collection, management, and storage of data. These applications include enhancement of storage and retrieval in a data system, integration of diverse systems, integration of semantic content on the web, and annotation of publications in a library setting.Successful application of ontologies has led to the creation of languages with the special purpose of implementing ontologies. A formalized ontology is an ontology expressed in accordance with the grammatical formation rules of an artificial language. Some existing ontology languages have been developed in order to serve specific functions that require expressibility limitations and expression of information in a manner that contributes to human misunderstanding and error. The most potentially detrimental effect is risked when an ontology is constructed in a language designed exclusively for computerized implementation. The result is a skewed representation of salient features of reality. An ontology development language has two purposes: one is to represent reality as accurately and completely as possible, the other is to achieve this in a manner that facilitates computerized implementation: these goals conflict. Validation requires expert human consensus, hence, an ontology should be developed in a language that is understandable to domain experts. However, such a language must be computer tractable, i.e., there must be a correspondence between the information expressed with a sentence and its grammatical structure such that information can be processed on the basis of syntax alone. LBFO will facilitate providing definitions and characterizations of features of reality in a way conformant with Basic Formal Ontology (BFO) thus ensuring maximal rigor and clarity. Since LBFO is a multi-sorted language, LBFO has resources to represent the ontological categories found in BFO and the universals defined in their terms in an economical and at the same time user-friendly way. BFO is a realist ontology in that it recognizes universals as an part of the world. BFO also recognizes the existence of both processes and continuants. A continuant is an individual that exists in full at each point in time in which it exists, a process is an individual that exists in stages and happens through time. Unlike a continuant, a process cannot be identified with any single stage at which it exists at a specific point in time. Capitalized variables range over universals, while lower-case variables range over individuals. Universal constants are upper-case. Individual constants are lower-case. The syntax of LBFO also distinguishes in a straightforward manner between variables for continuants, processes, and times. The syntax of LBFO contains precisely expressed grammatical-formation rules, so that its variables cannot be combined in a manner that results in category errors. The predicates of LBFO are such that the ontological category from which terms representing entities can be taken as arguments is specified in advance. Sentences which express category errors are not grammatically correct in LBFO.Since the demand for implementation often outstrips the demand for accurate representation, stand-alone ontologies are often left by the wayside. LBFO can serve as a bridge between domain experts, knowledge engineers, and implementation languages. The semantic apparatus of an FOL system serves as the basis for the models developed for implementation languages such as OWL and RDF. FOL is also a segregated dialect of Common Logic so there is a link to that international standard; hence, there is potential to develop middle-ware that maps LBFO to the variety of implementation languages that exist both now and in the future. Though there is much work to be done in perfecting LBFO, this first step in the process provides hope for achieving the goal of facilitating maximally accurate, rigorous representations of general features of reality.

Cancer nanotechnology research data are diverse. Ontologies that provide a unifying knowledge framework for annotation of data are necessary to facilitate the sharing and semantic integration of data for advancing the research via informatics methods. In this work, we report the development of NanoParticle Ontology (NPO) to support the terminological and informatics needs of cancer nanotechnology. The NPO is developed within the framework of the Basic Formal Ontology (BFO) using well-defined principles, and implemented in the Ontology Web Language (OWL). The NPO currently represents entities related to physical, chemical and functional descriptions of nanoparticles that are formulated and tested for applications in cancer diagnostics and therapeutics. Public releases of the NPO are available through the BioPortal web site, maintained by the National Center for Biomedical Ontology. Expansion of the scope and application of the NPO will depend on the needs of and feedback from the user community, and its adoption in nanoparticle database applications. As the NPO continues to grow, it will require a governance structure and well-organized community effort for the maintenance, review and development of the NPO.

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protege-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.

We present the design and development of NanoParticle Ontology (NPO). The ontology is implemented in the Ontology Web Language (OWL). The domain terms in NPO currently represent entities, which describe knowledge about physical, chemical, and functional properties of nanoparticles characterized in cancer nanotechnology research. The upper-level of NPO is formed using terms from the Basic Formal Ontology (BFO).

This paper addresses a family of issues surrounding the biological phenomenon of resistance and its representation in realist ontologies. The treatments of resistance terms in various existing ontologies are examined and found to be either overly narrow, internally inconsistent, or otherwise problematic. We propose a more coherent characterization of resistance in terms of what we shall call blocking dispositions, which are collections of mutually coordinated dispositions which are of such a sort that they cannot undergo simultaneous realization within a single bearer. A definition of ‘protective resistance’ is proposed for use in the Infectious Disease Ontology (IDO) and we show how this definition can be used to characterize the antibiotic resistance in Methicillin-Resistant Staphylococcus aureus (MRSA). The ontological relations between entities in our MRSA case study are used alongside a series of logical inference rules to illustrate logical reasoning about resistance. A description logic representation of blocking dispositions is also provided. We demonstrate that our characterization of resistance is sufficiently general to cover two other cases of resistance in the infectious disease domain involving HIV and malaria.

Numerous research groups are now utilizing Basic Formal Ontology (BFO) as an upper-level framework to assist in the organization and integration of biomedical information. This paper provides elucidation of the three BFO categories of function, role, and disposition, and considers two proposed sub-categories of artifactual function and bio-logical function. The motivation is to help advance the coherent treatment of functions, roles, and dispositions, to help provide the potential for more detailed classification, and to shed light on BFO’s general structure and use.

This paper reports on original results of the ACGT integrated project focusing on the design and development of a European Biomedical Grid infrastructure in support of multicentric, post genomic clinical trials on Cancer. The paper provides a presentation of the needs of users involved in post-genomic CTs, and presents such needs in the form of scenarios which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented and its services are classified and discussed. The Master Ontology on Cancer, been developed by the project, is presented and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.

The IPAP Schizophrenia Algorithm was originally designed in the form of a flow chart to help physicians optimise the treatment of schizophrenic patients. We examined the current version from the perspective of recent work on terminologies and ontologies thereby drawing on the resources of Basic Formal Ontology, and this with the objective to make the algorithm appropriate for Semantic Web applications. We found that Basic Formal Ontology is a rich enough theory to represent all the entities involved and that applying the theory to the IPAP schizophrenia algorithm results in a representation that can be used by software agents to perform monitoring and control in a referent tracking environment.

In this paper we apply a formal ontological framework in order to deconstruct two prominent approaches to navigation from cognitive robotics, the Spatial Semantic Hierarchy of Kuipers and the Route Graph of Krieg-Bruckner, Werner and others. The ontological framework is based on our current work on ontology specification, where we are investigating Masolo et al.'s Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) extended particularly for space and navigation by incorporating aspects of Smith et al.'s Basic Formal Ontology (BFO). Our conclusion is that ontology should necessarily play an important role in the design and modelling of cognitive robotic systems: comparability between approaches is improved, modelling gaps and weaknesses are highlighted, re-use of existing formalisations is facilitated, and extensions for interaction with other components, such as natural language systems, are directly supported.

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from the Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry.

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. The terms in OBCS including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed. The OBCS source code and documentation are available at: https://github.com/obcs/obcs . The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.

Background: 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. Problem: 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. Method: We recruited 8 BFO experts and asked them to classify 46 commonly known entities from the domain of travel with BFO entities. The tasks were delivered as part of a web survey. Results: We find that, even for a well understood general knowledge domain such as travel, the results of the manual classification tasks are highly inconsistent: the mean agreement of the participants with the classification decisions of an expert panel was only 51%, and the inter-rater agreement using Fleiss’ Kappa was merely moderate (0.52). We further follow up on the conjecture that the degree of classification consistency is correlated with the frequency the respective BFO classes are used in practice and find that this is only true to a moderate degree (0.52, Pearson). Conclusions: 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.

Background Ontology development is a rapidly growing area of research, especially in the life sciences domain. To promote collaboration and interoperability between different projects, the OBO Foundry principles require that these ontologies be open and non-redundant, avoiding duplication of terms through the re-use of existing resources. As current options to do so present various difficulties, a new approach, MIREOT, allows specifying import of single terms. Initial implementations allow for controlled import of selected annotations and certain classes of related terms. Findings OntoFox http://ontofox.hegroup.org/ is a web-based system that allows users to input terms, fetch selected properties, annotations, and certain classes of related terms from the source ontologies and save the results using the RDF/XML serialization of the Web Ontology Language (OWL). Compared to an initial implementation of MIREOT, OntoFox allows additional and more easily configurable options for selecting and rewriting annotation properties, and for inclusion of all or a computed subset of terms between low and top level terms. Additional methods for including related classes include a SPARQL-based ontology term retrieval algorithm that extracts terms related to a given set of signature terms and an option to extract the hierarchy rooted at a specified ontology term. OntoFox's output can be directly imported into a developer's ontology. OntoFox currently supports term retrieval from a selection of 15 ontologies accessible via SPARQL endpoints and allows users to extend this by specifying additional endpoints. An OntoFox application in the development of the Vaccine Ontology (VO) is demonstrated. Conclusions OntoFox provides a timely publicly available service, providing different options for users to collect terms from external ontologies, making them available for reuse by import into client OWL ontologies.

Background Ontologies are rapidly becoming a necessity for the design of efficient information technology tools, especially databases, because they permit the organization of stored data using logical rules and defined terms that are understood by both humans and machines. This has as consequence both an enhanced usage and interoperability of databases and related resources. It is hoped that IDOMAL, the ontology of malaria will prove a valuable instrument when implemented in both malaria research and control measures. Methods The OBOEdit2 software was used for the construction of the ontology. IDOMAL is based on the Basic Formal Ontology (BFO) and follows the rules set by the OBO Foundry consortium. Results The first version of the malaria ontology covers both clinical and epidemiological aspects of the disease, as well as disease and vector biology. IDOMAL is meant to later become the nucleation site for a much larger ontology of vector borne diseases, which will itself be an extension of a large ontology of infectious diseases (IDO). The latter is currently being developed in the frame of a large international collaborative effort. Conclusions IDOMAL, already freely available in its first version, will form part of a suite of ontologies that will be used to drive IT tools and databases specifically constructed to help control malaria and, later, other vector-borne diseases. This suite already consists of the ontology described here as well as the one on insecticide resistance that has been available for some time. Additional components are being developed and introduced into IDOMAL.