An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.

Toward a systematic conflict resolution framework for ontologies.

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later. RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies. CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.

The Infectious Disease Ontology in the age of COVID-19.

BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS: To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS: IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

BACKGROUND: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. OBJECTIVE: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. METHODS: The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. RESULTS: Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent's health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents' health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. CONCLUSIONS: The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

A scoping review of ontologies related to human behaviour change.

Ontologies are classification systems specifying entities, definitions and inter-relationships for a given domain, with the potential to advance knowledge about human behaviour change. A scoping review was conducted to: (1) identify what ontologies exist related to human behaviour change, (2) describe the methods used to develop these ontologies and (3) assess the quality of identified ontologies. Using a systematic search, 2,303 papers were identified. Fifteen ontologies met the eligibility criteria for inclusion, developed in areas such as cognition, mental disease and emotions. Methods used for developing the ontologies were expert consultation, data-driven techniques and reuse of terms from existing taxonomies, terminologies and ontologies. Best practices used in ontology development and maintenance were documented. The review did not identify any ontologies representing the breadth and detail of human behaviour change. This suggests that advancing behavioural science would benefit from the development of a behaviour change intervention ontology.

Interactive biomedical ontology matching.

Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems, it is necessary to identify the correspondences between these information, which is commonly known as biomedical ontology matching. However, it is a challenge to match biomedical ontologies, which dues to: (1) biomedical ontologies often possess tens of thousands of entities, (2) biomedical terminologies are complex and ambiguous. To efficiently match biomedical ontologies, in this paper, an interactive biomedical ontology matching approach is proposed, which utilizes the Evolutionary Algorithm (EA) to implement the automatic matching process, and gets a user involved in the evolving process to improve the matching efficiency. In particular, we propose an Evolutionary Tabu Search (ETS) algorithm, which can improve EA's performance by introducing the tabu search algorithm as a local search strategy into the evolving process. On this basis, we further make the ETS-based ontology matching technique cooperate with the user in a reasonable amount of time to efficiently create high quality alignments, and make use of EA's survival of the fittest to eliminate the wrong correspondences brought by erroneous user validations. The experiment is conducted on the Anatomy track and Large Biomedic track that are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show that our approach is able to efficiently exploit the user intervention to improve its non-interactive version, and the performance of our approach outperforms the state-of-the-art semi-automatic ontology matching systems.

Where to search top-K biomedical ontologies?

MOTIVATION: Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. RESULT: We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. CONCLUSION: The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. AVAILABILITY: The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark.

Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain.

OBJECTIVE AND BACKGROUND: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructured data. The success of smart healthcare applications such as clinical decision support systems, disease diagnosis systems, and healthcare management systems depends on knowledge that is understandable by machines to interpret and infer new knowledge from it. In this regard, ontological data models are expected to play a vital role to organize, integrate, and make informative inferences with the knowledge implicit in that unstructured data and represent the resultant knowledge in a form that machines can understand. However, constructing such models is challenging because they demand intensive labor, domain experts, and ontology engineers. Such requirements impose a limit on the scale or scope of ontological data models. We present a framework that will allow mitigating the time-intensity to build ontologies and achieve machine interoperability. METHODS: Empowered by linked biomedical ontologies, our proposed novel Automated Ontology Generation Framework consists of five major modules: a) Text Processing using compute on demand approach. b) Medical Semantic Annotation using N-Gram, ontology linking and classification algorithms, c) Relation Extraction using graph method and Syntactic Patterns, d), Semantic Enrichment using RDF mining, e) Domain Inference Engine to build the formal ontology. RESULTS: Quantitative evaluations show 84.78% recall, 53.35% precision, and 67.70% F-measure in terms of disease-drug concepts identification; 85.51% recall, 69.61% precision, and F-measure 76.74% with respect to taxonomic relation extraction; and 77.20% recall, 40.10% precision, and F-measure 52.78% with respect to biomedical non-taxonomic relation extraction. CONCLUSION: We present an automated ontology generation framework that is empowered by Linked Biomedical Ontologies. This framework integrates various natural language processing, semantic enrichment, syntactic pattern, and graph algorithm based techniques. Moreover, it shows that using Linked Biomedical Ontologies enables a promising solution to the problem of automating the process of disease-drug ontology generation.

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.

PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant amount of patients with a high level of clinical details, multicenter studies are necessary. A challenge in creating high quality Big Data studies involving several treatment centers is the lack of semantic interoperability between data sources. We present the ontology we developed to address this issue. METHODS: Radiation Oncology anatomical and target volumes were categorized in anatomical and treatment planning classes. International delineation guidelines specific to radiation oncology were used for lymph nodes areas and target volumes. Hierarchical classes were created to generate The Radiation Oncology Structures (ROS) Ontology. The ROS was then applied to the data from our institution. RESULTS: Four hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (.owl) format and made available online on Bioportal and GitHub under an Apache 2.0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our "record-and-verify" system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW). CONCLUSION: In this study we describe a new ontology, specific to radiation oncology, that reports all anatomical and treatment planning structures that can be delineated. This ontology will be used to integrate dosimetric data in the Assistance Publique-Hôpitaux de Paris CDW that stores data from 6.5 million patients (as of February 2017).

OWL-NETS: Transforming OWL Representations for Improved Network Inference.

Our knowledge of the biological mechanisms underlying complex human disease is largely incomplete. While Semantic Web technologies, such as the Web Ontology Language (OWL), provide powerful techniques for representing existing knowledge, well-established OWL reasoners are unable to account for missing or uncertain knowledge. The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge. Therefore, robust methods which facilitate inductive inference on rich OWL-encoded knowledge are needed. Here, we propose OWL-NETS (NEtwork Transformation for Statistical learning), a novel computational method that reversibly abstracts OWL-encoded biomedical knowledge into a network representation tailored for network inference. Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant hypotheses. Further, the lossless transformation of OWL-NETS allows for seamless integration of inferred edges back into the original knowledge base, extending its coverage and completeness.

Improving precision in concept normalization.

Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distribution of false positive results, we describe a strategy for increasing precision, using a variety of both pre-processing and post-processing methods. They draw on both knowledge-based and frequentist approaches to modeling language. Based on an existing high-performance biomedical concept recognition pipeline and a previously published manually annotated corpus, we apply this hybrid rationalist/empiricist strategy to concept normalization for eight different ontologies. Which approaches did and did not improve precision varied widely between the ontologies.

The anatomy of phenotype ontologies: principles, properties and applications.

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Ontology-based automatic identification of public health-related Turkish tweets.

Social media analysis, such as the analysis of tweets, is a promising research topic for tracking public health concerns including epidemics. In this paper, we present an ontology-based approach to automatically identify public health-related Turkish tweets. The system is based on a public health ontology that we have constructed through a semi-automated procedure. The ontology concepts are expanded through a linguistically motivated relaxation scheme as the last stage of ontology development, before being integrated into our system to increase its coverage. The ultimate lexical resource which includes the terms corresponding to the ontology concepts is used to filter the Twitter stream so that a plausible tweet subset, including mostly public-health related tweets, can be obtained. Experiments are carried out on two million genuine tweets and promising precision rates are obtained. Also implemented within the course of the current study is a Web-based interface, to track the results of this identification system, to be used by the related public health staff. Hence, the current social media analysis study has both technical and practical contributions to the significant domain of public health.

The Ontology for Biomedical Investigations.

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.

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

BACKGROUND AND OBJECTIVES: The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. METHODS: A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. RESULTS: The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. CONCLUSIONS: The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach.

[Stochastic simulation of the process of matching of the ages of the human and the experimental mammalian].

The aim of the article is to work out the simple approach for matching of the ages of the human and mammalian. The model is based on the analysis of the counting processes of the proper ontogenetic events (such as the emergence of the first molars, the first ovulation, almost complete cessation of growth, menopause). The choice of the events is reduced to the claim of being observable. The matching (and proper concordance) are useful for the choice of the individual regimens of medical treatment.

Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective.

The paper presents an overview of approaches to encoding uncertain causal knowledge in formal ontologies and demonstrates how these approaches can be used in a semantic-driven application for public health using the Population Health Record (PopHR) platform as an example.

Interaction relation ontology learning.

Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.