Wikidata: A large-scale collaborative ontological medical database

2008

The meeting focused on uses of ontologies, with a special focus on spatial ontologies, in addressing the ever increasing needs faced by biology and medicine to cope with ever expanding quantities of data. To provide effective solutions computers need to integrate data deriving from myriad heterogeneous sources by bringing the data together within a single framework. The meeting brought together leaders in the field of what are called 'top-level ontologies' to address this issue, and to establish strategies among leaders in the field of biomedical ontology for the creation of interoperable biomedical ontologies which will serve the goal of useful data integration.

Briefings in Bioinformatics, 2013

Semantic web technologies offer an approach to data integration and sharing, even for resources developed independently or broadly distributed across the web. This approach is particularly suitable for scientific domains that profit from large amounts of data that reside in the public domain and that have to be exploited in combination.

PLoS ONE, 2008

Springer eBooks, 2013

Medical science monitor: international medical journal of experimental and clinical research

HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics. We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -2000 plug-in. The project is saved in RDFS/RDF. The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Prot g -2000 RDF output are discussed. By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

2008

Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing, and decision support. The National Center for Biomedical Ontology is developing BioPortal, a Web-based system that serves as a repository for biomedical ontologies. BioPortal defines relationships among those ontologies and between the ontologies and online data resources such as PubMed, ClinicalTrials.gov, and the Gene Expression Omnibus (GEO). BioPortal supports not only the technical requirements for access to biomedical ontologies either via Web browsers or via Web services, but also community-based participation in the evaluation and evolution of ontology content. BioPortal enables ontology users to learn what biomedical ontologies exist, what a particular ontology might be good for, and how individual ontologies relate to one another. BioPortal is available online at http://bioportal.bioontology.org.

2007

The paper introduces basic features of a novel ontology integration framework that explicitely takes the dynamics and data-intensiveness of many practical application scenarios into account. We motivate our research partially by the needs of bio-medicine scenarios that have been recently identified within the search for semantics-enabled solutions. In this context, we show a concrete example of the integration process in the life-sciences settings. Moreover, we elaborate a possible bio-medicine industry application domain of the presented framework and explain the benefits of the proposed semantic solution.

One of the main problems of biomedical informatics in the effort to increase its contribution in knowledge retrieval and decision making is the integration of ever-increasing amounts of information and data from multiple heterogeneous sources and domains-clinical, medical, biological etc. The paper proposes an ontology based approach for integration of biomedical data and information using the Linked Open Data vocabularies and a D2RQ-mapped database. A simple example of semantic integration of heterogeneous biomedical and health data sources is given.

2012

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and use ontologies and terminologies in biomedicine. The centerpiece of the National Center for Biomedical Ontology is a web-based resource known as BioPortal. BioPortal makes available for research in computationally useful forms more than 270 of the world's biomedical ontologies and terminologies, and supports a wide range of web services that enable investigators to use the ontologies to annotate and retrieve data, to generate value sets and special-purpose lexicons, and to perform advanced analytics on a wide range of biomedical data.

OMICS: A Journal of Integrative Biology, 2006

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease. This paper is part of the special issue of OMICS on data standards.

npj digital medicine, 2019

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the widespread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Journal of …, 2011

Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.