Public Ontologies Research Articles

HeNeCOn: An ontology for integrative research in Head and Neck cancer

BackgroundHead and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge. ObjectiveThis work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology). MethodsFirst, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians. ResultsHeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management. ConclusionHeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies.

Towards an archaeology of rubber

This article aims to address the impacts of rubber via historical and contemporary archaeology of the Amazon. Through an “archaeology of rubber” a notion of modernity is examined here. From the creation of rubber gloves to snow boots and tires, rubber has allowed humans to expand their interactions with the environment. As these interactions expanded, the consolidation of the Industrial Revolution and Occidentalism entangled actors in a complex web of meanings, becomings and agencies in opposition to local knowledge. Through a plural and multispecies approach, this article places the study of rubber’s materiality in the field of the archaeology of capitalism and modernity. Also, through oral history, deep archival research and public archaeology, local ontologies and materialities offer contemporary archaeology a more elastic view, aimed at widening perceptions of a global story.

FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

Purpose One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles. Acquisition and validation methods Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open-source Ontology-Guided Radiomics Analysis Workflow (O-RAW). We reshaped the output of O-RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission. Data format The accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross-reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations. Potential applications The public availability of these data resources is intended to support radiomics features replication, repeatability, and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability are supported by referencing existing public ontologies. The RDF data are readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submission: https://gitlab.com/UM-CDS/FAIR-compliant_clinical_radiomics_and_DICOM_metadata.

FAIR-compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head-Neck1 TCIA collections.

PurposeOne of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles.Acquisition and validation methodsOverall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open‐source Ontology‐Guided Radiomics Analysis Workflow (O‐RAW). We reshaped the output of O‐RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission.Data formatThe accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross‐reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations.Potential applicationsThe public availability of these data resources is intended to support radiomics features replication, repeatability, and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability are supported by referencing existing public ontologies. The RDF data are readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submission: https://gitlab.com/UM‐CDS/FAIR‐compliant_clinical_radiomics_and_DICOM_metadata.

Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome–phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance.

The Additivity Project: Achieving additivity of structured taxonomic character data by persistently linking them to individual specimens

Herbarium specimens have always played a central role in the classical disciplines of plant sciences and the global digitisation efforts now open new horizons. To make full use of the inherent possibilities of specimen based taxonomic descriptions corresponding workflows are needed. A crucial step in the comparative analyses of organisms is the preparation of a character matrix to record and compare the morphological variation of taxa on the basis of individual specimens. This project focuses on the optimisation of the taxonomic research process with respect to delimitation and characterisation (“descriptions”) of taxa (Henning et al. 2018). The angiosperm order Caryophyllales provides exemplar use cases through cooperation with the Global Caryophyllales Initiative (Borsch et al. 2015). The workflow for sample data handling (Kilian et al. 2015), implemented on the EDIT Platform for Cybertaxonomy (http://www.cybertaxonomy.org, Ciardelli et al. 2009), has been extended to support additive characterisation of taxa via specimen character data. The Common Data Model (CDM), already supporting persistent inter-linking of specimens and their metadata (Plitzner et al. 2017), has been adapted to facilitate specimen descriptions with characters constructed from the combination of structure and property terms and their corresponding states. Semantic web technology is used to establish and continuously elaborate expert community-coordinated exemplar vocabularies with term ontologies and explanations for characters and states (GFBio Terminology Service, Karam et al. 2016). Character data are recorded and stored in structured form in character state matrices for individual specimens instead of taxa, which allows generation of taxon characterisations by aggregating the data sets for the individual specimens included. Separating characters in structures and properties, which are based on concepts in public ontologies, guarantees a high visibility and instant re-usability of these character data. Taking into account that taxon concepts evolve during the iterative knowledge generation process in systematic biology, additivity of character data from specimen to taxon level therefore greatly facilitates the construction and reproducibility of taxon characterisations from changing specimen and character data sets.

Semantic Web Annotation using Deep Learning with Arabic Morphology

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language.

Ontology federation

An Ontology defines a common vocabulary across diverse platforms, supporting machine-interpretable semantics of the domain concepts along with associated relations. Ontologies support the definitions of a class which defines concepts in a domain and exist as the central focus. Proprietary Ontologies allow for internally developed machine-usable content to be leveraged for greater purpose. In this context, Industrial Ontologies can be considered first and foremost, an integration technology, supporting connections between any number of disparate data sources. By this application, it is possible to leverage proprietary and public ontologies thus supporting a Federated Ontology. This paper explores the current methods and technologies of federated Ontologies. To this goal, we summarize the current state of publically available ontologies examining how they are currently utilized, their application challenges and a realistic assessment of their potential.

Semantic Annotation of Arabic Web Documents using Deep Learning

The vision of Semantic Web is to have a Web of things instead of Web of documents in a form that can be processed by machines. This vision could be achieved on the existing Web using semantic annotation based on common and public ontologies. Due to exponential growth and the huge size of the Web sources, there is a need to have fast and automatic semantic annotation of Web documents. Arabic language received less attention in semantic Web research as compared to Latin languages especially in the field of semantic annotation. The aim of this paper is to investigate the feasibility of using word embeddings from deep learning algorithms for semantic annotation of Arabic Web documents. To evaluate the performance of the proposed framework, food, nutrition, and health ontologies were used to annotate some related Web documents. For a given set of Arabic documents and ontologies, the framework produces annotations of these documents using different output formats. The initial results show a promising performance which will support the research in the Semantic Web with respect to Arabic language. The proposed framework could be used for building semantic Web application and semantic search engines for Arabic Language.

An efficient and large-scale reasoning method for the semantic Web

We present an extended version of the źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ taxonomic reasoner for large ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ system is based upon the źźźźźź$\mathcal {O}\mathcal {S}\mathcal {F}$ formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional mapping between źźźźźź$\mathcal {O}\mathcal {S}\mathcal {F}$ graph structures and the Resource Description Framework (RDF) allowing a translation from źźźźźź$\mathcal {O}\mathcal {S}\mathcal {F}$ queries into SPARQL for retrieving instances from an RDF triplestore. We carried out comparative performance evaluation experiments using źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ as well as well-known Semantic Web reasoners (such as FaCT++, Pellet, HermiT, TrOWL, and RacerPro) on very large public ontologies. For the same queries on the same ontologies, the results achieved by źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ were compared to those obtained by all the other reasoners. The results of experiments show that źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ consistently performs on a par with the fastest systems for concept classification, and several orders of magnitude more efficiently in terms of response time for Boolean query-answering over attributed concepts, as well as for ABox triplestore querying. The latter result is irrespective of the triplestore management used because the źźźźźźźźźź$\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}$ reasoner uses its knowledge to optimize SPARQL queries before submitting them to the triplestore.

Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach

BackgroundThe high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services’ interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies. ObjectiveTo facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data. Materials and methodsWe developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data. ResultsWe developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built. DiscussionLinked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine interpretable semantics to the CDS service description alleviating the challenges on interoperability and reuse. Linked Services allow for building ‘digital libraries’ of distributed CDS services that can be hosted and maintained in different organizations.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Technology Outlook: Federated Ontologies and Industrial Applications

Proprietary Ontologies allow for internally developed machine-usable content to be leveraged for greater purpose. In this context, Industrial Ontologies can be considered first and foremost, an integration technology, supporting connections between any numbers of disparate data sources. By this application, it is possible to leverage proprietary and public ontologies thus supporting a Federated Ontology. This paper explores the potential of a Federated Ontology for the purpose of supporting reasoning in the manufacturing sector. To this goal we summarize the current state of publically available ontologies examining how they are currently utilized, their application challenges and a realistic assessment of their potential.

Towards measurement of structural complexity for ontologies

Ontologies represent knowledge of a particular domain and form an elementary unit for inference techniques on the semantic web. It is important to measure the complexity of ontologies in order for users to better understand, maintain, reuse and integrate them. Existing measures for the complexity of an ontology either handle complexity at schema and instance levels or define subjective parameters to measure ontology complexity. To this end, we present a semi-automated framework to measure the structural complexity at different abstraction levels of an ontology. Moreover, our metrics leverage the information provided by ontology editors, which can be useful to the ontology designer for selection of the ontology with optimum complexity among alternative ontologies. Our framework is inspired by the concept of component-based software complexity metrics and its metrics have been validated against Briand's and Weyuker's benchmarks. We have also performed comparative analysis on public ontologies.

Semantic Annotation of Arabic Web Resources Using Semantic Web Services

The vision of semantic Web is to have a Web of things instead of Web of documents in a form that can be processed by machines. This vision could be achieved on the existing Web using semantic annotation based on common and public ontologies. Due to exponential growth and huge size of the Web sources, there is a need to have fast and automatic semantic annotation services of Web documents. Since Arabic language received less attention in semantic Web research as compared to Latin languages especially in the field of semantic annotation. This motivates us in this paper to present semantic Web services that support the semantic annotation of Arabic language documents. The services accept documents and ontologies and produce annotations of these documents using different output formats. The proposed services could be used for building semantic Web applications and semantic search engines for Arabic Language. To evaluate the performance of these services, a set of ontologies were used with pre-annotated documents related to those ontologies. The initial results show a promising performance which will support the research in the semantic Web with respect to Arabic language.

Mouse Phenome Database

The Mouse Phenome Database (MPD; phenome.jax.org) was launched in 2001 as the data coordination center for the international Mouse Phenome Project. MPD integrates quantitative phenotype, gene expression and genotype data into a common annotated framework to facilitate query and analysis. MPD contains >3500 phenotype measurements or traits relevant to human health, including cancer, aging, cardiovascular disorders, obesity, infectious disease susceptibility, blood disorders, neurosensory disorders, drug addiction and toxicity. Since our 2012 NAR report, we have added >70 new data sets, including data from Collaborative Cross lines and Diversity Outbred mice. During this time we have completely revamped our homepage, improved search and navigational aspects of the MPD application, developed several web-enabled data analysis and visualization tools, annotated phenotype data to public ontologies, developed an ontology browser and released new single nucleotide polymorphism query functionality with much higher density coverage than before. Here, we summarize recent data acquisitions and describe our latest improvements.

A modular framework for gene set analysis integrating multilevel omics data

Modern high-throughput methods allow the investigation of biological functions across multiple ‘omics’ levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.

Yes, we can! Lessons from using Linked Open Data (LOD) and public ontologies to contextualize and enrich experimental data

(2) The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside. Luciano JS, Andersson B, Batchelor C, Bodenreider O, Clark T, Denney CK, Domarew C, Gambet T, Harland L, Jentzsch A, Kashyap V, Kos P, Kozlovsky J, Lebo T, Marshall SM, McCusker JP, McGuinness DL, Ogbuji C, Pichler E, Powers RL, Prud’hommeaux E, Samwald M, Schriml L, Tonellato PJ, Whetzel PL, Zhao J, Stephens S, Dumontier M. J.Biomed.Semantics 2011; 2(Suppl 2):S1 (3) Does network analysis of integrated data help understanding how alcohol affects biological functions? Results of a semantic approach to biomarker discovery. Gombocz EA, A.J. Higgins AJ, Hurban P, Lobenhofer EK, Crews FT, Stanley RA, Rockey C, Nishimura T. 2008 Sept.29-Oct.1.Biomarker Discovery Summit, Philadelphia, PA. Yes, we can! Lessons from Using Linked Open Data (LOD) and Public Ontologies to Contextualize and Enrich Experimental Data

Ontology-enriched multi-document summarization in disaster management using submodular function

In disaster management, a myriad of news and reports relevant to the disaster may be recorded in the form of text document. A challenging problem is how to provide concise and informative reports from a large collection of documents, to help domain experts analyze the trend of the disaster. In this paper, we explore the feasibility of using a domain-specific ontology to facilitate the summarization task, and propose TELESUM, an ontology-enriched multi-document summarization approach, where the submodularity hidden in among ontological concepts is investigated. Empirical experiments on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy and effectiveness of TELESUM in disaster management. Further, our proposed framework can be extended to summarizing general documents by employing public ontologies, e.g., Wikipedia. Extensive evaluation on the generalized framework is conducted on DUC04-05 datasets, and shows that our method is competitive with other approaches.

Voronto: mapper for expression data to ontologies

Several techniques analyze gene expression data from the point of view of biological ontologies. These methods focus on statistical and numerical analyses, but there is an additional need for fast, global and interactive visualizations of gene expression data in the context of biological ontologies. Voronto addresses these needs with an easy to use integration of custom gene expression data with custom or public ontologies. In order to do that, it implements Voronoi diagrams where ontologies are mapped with gene expression, providing a quick hierarchy browsing and integration with ontology's resources. The tool is available at http://vis.usal.es/voronto. Source code available at https:/github.com/rodrigoSantamaria/Voronto.