An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method

Randomized controlled trials (RCTs) are one of the least biased sources of clinical research evidence, and are therefore a critical resource for the practice of evidence-based medicine. With over 10,000 new RCTs indexed in Medline each year, knowledge systems are needed to help clinicians translate evidence into practice. Common ontologies for RCTs and other domains would facilitate the development of these knowledge systems. However, no standard method exists for developing domain ontologies. In this paper, we describe a new systematic approach to specifying and evaluating the conceptual content of ontologies. In this method, called competency decomposition, the target task for an ontology is hierarchically decomposed into subtasks and methods, and the ontology content is specified by identifying the domain information required to complete each of the subtasks. We illustrate the use of this competency decomposition approach for the content specification and evaluation of an RCT ontology for evidence-based practice.     

Ontology development for unified traditional Chinese medical language system

Traditional Chinese medicine (TCM) as a complete knowledge system researches into human health conditions via a different approach compared to orthodox medicine. We are developing a unified traditional Chinese medical language system (UTCMLS) through an ontology approach that will support TCM language knowledge storage, concept-based information retrieval and information integration. UTCMLS is a huge knowledge project, which is a broad collaboration of 16 distributed groups, most of them with no prior experience of formal ontology development. Therefore, the cooperative and comprehensive ontology engineering is crucial. We use Protégé 2000 for ontology development of concepts and relationships that represent the domain and that will permit storage of TCM knowledge. This paper focuses on the methodology, design and development of ontology for UTCMLS.     

Desiderata for domain reference ontologies in biomedicine

Domain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain. An example of reference ontology in biomedical informatics is the Foundational Model of Anatomy (FMA), an ontology of anatomy that covers the entire range of macroscopic, microscopic, and subcellular anatomy. The purpose of this paper is to explore how two domain reference ontologies--the FMA and the Chemical Entities of Biological Interest (ChEBI) ontology, can be used (i) to align existing terminologies, (ii) to infer new knowledge in ontologies of more complex entities, and (iii) to manage and help reasoning about individual data. We analyze those kinds of usages of these two domain reference ontologies and suggest desiderata for reference ontologies in biomedicine. While a number of groups and communities have investigated general requirements for ontology design and desiderata for controlled medical vocabularies, we are focusing on application purposes. We suggest five desirable characteristics for reference ontologies: good lexical coverage, good coverage in terms of relations, compatibility with standards, modularity, and ability to represent variation in reality.     

COnto–Diff: generation of complex evolution mappings for life science ontologies

Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management. We propose a novel approach COnto–Diff to determine an expressive and invertible diff evolution mapping between given versions of an ontology. Our approach first matches the ontology versions and determines an initial evolution mapping consisting of basic change operations (insert/update/delete). To semantically enrich the evolution mapping we adopt a rule-based approach to transform the basic change operations into a smaller set of more complex change operations, such as merge, split, or changes of entire subgraphs. The proposed algorithm is customizable in different ways to meet the requirements of diverse ontologies and application scenarios. We evaluate the proposed approach for large life science ontologies including the Gene Ontology and the NCI Thesaurus and compare it with PromptDiff. We further show how the Diff results can be used for version management and annotation migration in collaborative curation.     

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.     

Semantic enrichment for medical ontologies

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a two-level structure in the medical field has led to notable advances in terminologies and ontologies. However, most ontologies and terminologies do not have such a two-level structure. Therefore, we present a method, called semantic enrichment, which generates a two-level ontology from a given one-level terminology and an auxiliary two-level ontology. During semantic enrichment, concepts of the one-level terminology are assigned to semantic types, which are the building blocks of the upper level of the auxiliary two-level ontology. The result of this process is the desired new two-level ontology. We discuss semantic enrichment of two example terminologies and how we approach the implementation of semantic enrichment in the medical domain. This implementation performs a major part of the semantic enrichment process with the medical terminologies, with difficult cases left to a human expert.     

A novel approach for connecting temporal-ontologies with blood flow simulations

In this paper an approach for developing a temporal domain ontology for biomedical simulations is introduced. The ideas are presented in the context of simulations of blood flow in aneurysms using the Lattice Boltzmann Method. The advantages in using ontologies are manyfold: On the one hand, ontologies having been proven to be able to provide medical special knowledge e.g., key parameters for simulations. On the other hand, based on a set of rules and the usage of a reasoner, a system for checking the plausibility as well as tracking the outcome of medical simulations can be constructed. Likewise, results of simulations including data derived from them can be stored and communicated in a way that can be understood by computers. Later on, this set of results can be analyzed. At the same time, the ontologies provide a way to exchange knowledge between researchers. Lastly, this approach can be seen as a black-box abstraction of the internals of the simulation for the biomedical researcher as well. This approach is able to provide the complete parameter sets for simulations, part of the corresponding results and part of their analysis as well as e.g., geometry and boundary conditions. These inputs can be transferred to different simulation methods for comparison. Variations on the provided parameters can be automatically used to drive these simulations. Using a rule base, unphysical inputs or outputs of the simulation can be detected and communicated to the physician in a suitable and familiar way. An example for an instantiation of the blood flow simulation ontology and exemplary rules for plausibility checking are given.     

Biomedical Ontologies to Guide AI Development in Radiology

The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain’s terms through their relationships with other terms in the ontology. Those relationships, then, define the terms’ semantics, or “meaning.” Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA’s RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image–based machine learning, radiomics, and planning.

     

Text-mining approach to evaluate terms for ontology development

Developing ontologies to account for the complexity of biological systems requires the time intensive collaboration of many participants with expertise in various fields. While each participant may contribute to construct a list of terms for ontology development, no objective methods have been developed to evaluate how relevant each of these terms is to the intended domain. We have developed a computational method based on a hypergeometric enrichment test to evaluate the relevance of such terms to the intended domain. The proposed method uses the PubMed literature database to evaluate whether each potential term for ontology development is overrepresented in the abstracts that discuss the particular domain. This evaluation provides an objective approach to assess terms and prioritize them for ontology development.     

面向精准医学的生物医学本体资源存储和应用平台

在过去10余年中,本体广泛应用于生物医学数据分析、检索、整合和再利用中。本体作为一种特殊类型的数据资源,数据量也在迅速增加。为了促进精准医疗领域数据集的整合,并为国内用户提供本体数据资源服务,构建MedPortal本体资源存储和应用平台。通过复用NCBO BioPortal技术,搭建MedPotal软件框架。遴选精准医学相关本体,建立本体资源库。对原框架中的代码和本体处理工具进行修正和完善,使之能够在本体稳定运行的基础上满足大批量数据的自动化处理。目前,该平台已整合42个生物医学本体,建立了本体之间术语映射关系,通过页面和REST API方式,提供术语检索、本体映射、数据标准化注释等本体应用服务(http://medportal.bmicc.cn)。MedPortal本体平台将为生物医学数据整合提供帮助。     

An empirical analysis of ontology reuse in BioPortal

Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies. Content reuse enables a consistent representation of a domain and reusing content can save an ontology author significant time and effort. Prior studies have investigated the existence of reused terms among the ontologies in the NCBO BioPortal, but as of yet there has not been a study investigating how the ontologies in BioPortal utilize reused content in the modeling of their own content. In this study we investigate how 355 ontologies hosted in the NCBO BioPortal reuse content from other ontologies for the purposes of creating new ontology content. We identified 197 ontologies that reuse content. Among these ontologies, 108 utilize reused classes in the modeling of their own classes and 116 utilize reused properties in class restrictions. Current utilization of reuse and quality issues related to reuse are discussed.     

Cross-domain targeted ontology subsets for annotation: The case of SNOMED CORE and RxNorm

The benefits of using ontology subsets versus full ontologies are well-documented for many applications. In this study, we propose an efficient subset extraction approach for a domain using a biomedical ontology repository with mappings, a cross-ontology, and a source subset from a related domain. As a case study, we extracted a subset of drugs from RxNorm using the UMLS Metathesaurus, the NDF-RT cross-ontology, and the CORE problem list subset of SNOMED CT. The extracted subset, which we termed RxNorm/CORE, was 4% the size of the full RxNorm (0.4% when considering ingredients only). For evaluation, we used CORE and RxNorm/CORE as thesauri for the annotation of clinical documents and compared their performance to that of their respective full ontologies (i.e., SNOMED CT and RxNorm). The wide range in recall of both CORE (29–69%) and RxNorm/CORE (21–35%) suggests that more quantitative research is needed to assess the benefits of using ontology subsets as thesauri in annotation applications. Our approach to subset extraction, however, opens a door to help create other types of clinically useful domain specific subsets and acts as an alternative in scenarios where well-established subset extraction techniques might suffer from difficulties or cannot be applied.     

A network-theoretic approach for decompositional translation across Open Biological Ontologies

Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, composite terms in these ontologies need to be translated into atomic or primitive terms in other, orthogonal ontologies, for example, gluconeogenesis (biological process term) to glucose (chemical ontology term). Identifying such decompositional ontology translations is a challenging problem. In this paper, we propose a network-theoretic approach based on the structure of the integrated OBO relationship graph. We use a network-theoretic measure, called the clustering coefficient, to find relevant atomic terms in the neighborhood of a composite term. By eliminating the existing GO to ChEBI Ontology mappings from OBO, we evaluate whether the proposed approach can re-identify the corresponding relationships. The results indicate that the network structure provides strong cues for decompositional ontology translation and the existing relationships can be used to identify new translations.     

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly $50\text{,}000$ classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.     

Knowledge-data integration for temporal reasoning in a clinical trial system

Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing and integrating temporal data and domain knowledge is difficult with the database technologies used in most clinical research systems. There is often a disconnect between the database representation of research data and corresponding domain knowledge of clinical research concepts. In this paper, we present a set of methodologies for undertaking ontology-based specification of temporal information, and discuss their application to the verification of protocol-specific temporal constraints among clinical trial activities. Our approach allows knowledge-level temporal constraints to be evaluated against operational trial data stored in relational databases. We show how the Semantic Web ontology and rule languages OWL and SWRL, respectively, can support tools for research data management that automatically integrate low-level representations of relational data with high-level domain concepts used in study design.     

Measuring the level of activity in community built bio-ontologies

In this paper we explore the measurement of activity in ontology projects as an aspect of community ontology building. When choosing whether to use an ontology or whether to participate in its development, having some knowledge of how actively that ontology is developed is an important issue. Our knowledge of biology grows and changes and an ontology must adapt to keep pace with those changes and also adapt with respect to other ontologies and organisational principles. In essence, we need to know if there is an ‘active’ community involved with a project or whether a given ontology is inactive or moribund. We explore the use of additions, deletions and changes to ontology files, the regularity and frequency of releases, and the number of ontology repository updates to an ontology as the basis for measuring activity in an ontology. We present our results of this study, which show a dramatic range of activity across some of the more prominent community ontologies, illustrating very active and mature efforts through to those which appear to have become dormant for a number of possible reasons. We show that global activity within the community has remained at a similar level over the last 2 years. Measuring additions, deletions and changes, together with release frequency, appear to be useful metrics of activity and useful pointers towards future behaviour. Measuring who is making edits to ontologies is harder to capture; this raises issues of record keeping in ontology projects and in micro-credit, although we have identified one ontologist that appears influential across many community efforts; a Super-Ontologist. We also discuss confounding factors in our activity metric and discuss how it can be improved and adopted as an assessment criterion for community ontology development. Overall, we show that it is possible to objectively measure the activity in an ontology and to make some prediction about future activity.     

Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn

Bio-ontology development is a resource-consuming task despite the many open source ontologies available for reuse. Various strategies and tools for bottom-up ontology development have been proposed from a computing angle, yet the most obvious one from a domain expert perspective is unexplored: the abundant diagrams in the sciences. To speed up and simplify bio-ontology development, we propose a detailed, micro-level, procedure, DiDOn, to formalise such semi-structured biological diagrams availing also of a foundational ontology for more precise and interoperable subject domain semantics. The approach is illustrated using Pathway Studio as case study.     

The unexpected high practical value of medical ontologies

Ontology is no longer a mere research topic, but its relevance has been recognized in several practical fields. Current applications areas include natural language translation, e-commerce, geographic information systems, legal information systems and biology and medicine. It is the backbone of solid and effective applications in health care and can help to build more powerful and more interoperable medical information systems. The design and implementation of ontologies in medicine is mainly focused on the re-organization of medical terminologies. This is obviously a difficult task and requires a deep analysis of the structure and the concepts of such terminologies, in order to define domain ontologies able to provide both flexibility and consistency to medical information systems. The aim of this special issue of Computers in Biology and Medicine is to report the current evolution of research in biomedical ontologies, presenting both papers devoted to methodological issues and works with a more applicative emphasis.     

A multi-ontology approach to annotate scientific documents based on a modularization technique

Scientific text annotation has become an important task for biomedical scientists. Nowadays, there is an increasing need for the development of intelligent systems to support new scientific findings. Public databases available on the Web provide useful data, but much more useful information is only accessible in scientific texts. Text annotation may help as it relies on the use of ontologies to maintain annotations based on a uniform vocabulary. However, it is difficult to use an ontology, especially those that cover a large domain. In addition, since scientific texts explore multiple domains, which are covered by distinct ontologies, it becomes even more difficult to deal with such task. Moreover, there are dozens of ontologies in the biomedical area, and they are usually big in terms of the number of concepts. It is in this context that ontology modularization can be useful. This work presents an approach to annotate scientific documents using modules of different ontologies, which are built according to a module extraction technique. The main idea is to analyze a set of single-ontology annotations on a text to find out the user interests. Based on these annotations a set of modules are extracted from a set of distinct ontologies, and are made available for the user, for complementary annotation. The reduced size and focus of the extracted modules tend to facilitate the annotation task. An experiment was conducted to evaluate this approach, with the participation of a bioinformatician specialist of the Laboratory of Peptides and Proteins of the IOC/Fiocruz, who was interested in discovering new drug targets aiming at the combat of tropical diseases.