DMTO: a realistic ontology for standard diabetes mellitus treatment.

J Biomed Semantics. 2018 Feb 06;9(1):8

Authors: El-Sappagh S, Kwak D, Ali F, Kwak KS

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

PMID: 29409535 [PubMed - in process]

Semantic annotation of consumer health questions.

BMC Bioinformatics. 2018 Feb 06;19(1):34

Authors: Kilicoglu H, Ben Abacha A, Mrabet Y, Shooshan SE, Rodriguez L, Masterton K, Demner-Fushman D

Abstract
BACKGROUND: Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.
RESULTS: The resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence.
CONCLUSIONS: To our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.

PMID: 29409442 [PubMed - in process]

Rich semantic networks applied to schizophrenia: A new framework.

Schizophr Res. 2016 10;176(2-3):454-455

Authors: De Deyne S, Elvevåg B, Hui CLM, Poon VWY, Chen EYH

PMID: 27245710 [PubMed - indexed for MEDLINE]

A systematic approach to analyze the social determinants of cardiovascular disease.

PLoS One. 2018;13(1):e0190960

Authors: Martínez-García M, Salinas-Ortega M, Estrada-Arriaga I, Hernández-Lemus E, García-Herrera R, Vallejo M

Abstract
Cardiovascular diseases are the leading cause of human mortality worldwide. Among the many factors associated with the etiology, incidence, and evolution of such diseases; social and environmental issues constitute an important and often overlooked component. Understanding to a greater extent the scope to which such social determinants of cardiovascular diseases (SDCVD) occur as well as the connections among them would be useful for public health policy making. Here, we will explore the historical trends and associations among the main SDCVD in the published literature. Our aim will be finding meaningful relations among those that will help us to have an integrated view on this complex phenomenon by providing historical context and a relational framework. To uncover such relations, we used a data mining approach to the current literature, followed by network analysis of the interrelationships discovered. To this end, we systematically mined the PubMed/MEDLINE database for references of published studies on the subject, as outlined by the World Health Organization's framework on social determinants of health. The analyzed structured corpus consisted in circa 1190 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor. The use of data analytics techniques allowed us to find a number of non-trivial connections among SDCVDs. Such relations may be relevant to get a deeper understanding of the social and environmental issues associated with cardiovascular disease and are often overlooked by traditional literature survey approaches, such as systematic reviews and meta-analyses.

PMID: 29370200 [PubMed - indexed for MEDLINE]

Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration.

BMC Bioinformatics. 2018 Feb 01;19(1):30

Authors: Xin J, Afrasiabi C, Lelong S, Adesara J, Tsueng G, Su AI, Wu C

Abstract
BACKGROUND: Application Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation (JSON) as their primary data format. While usage of a common data format offers significant advantages, that alone is not sufficient for rich integrative queries across APIs.
RESULTS: Here, we have implemented JSON for Linking Data (JSON-LD) technology on the BioThings APIs that we have developed, MyGene.info , MyVariant.info and MyChem.info . JSON-LD provides a standard way to add semantic context to the existing JSON data structure, for the purpose of enhancing the interoperability between APIs. We demonstrated several use cases that were facilitated by semantic annotations using JSON-LD, including simpler and more precise query capabilities as well as API cross-linking.
CONCLUSIONS: We believe that this pattern offers a generalizable solution for interoperability of APIs in the life sciences.

PMID: 29390967 [PubMed - in process]

Semantic and syntactic interoperability in online processing of big Earth observation data.

Int J Digit Earth. 2018;11(1):95-112

Authors: Sudmanns M, Tiede D, Lang S, Baraldi A

Abstract
The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

PMID: 29387171 [PubMed]

Listeners and Readers Generalize Their Experience With Word Meanings Across Modalities.

J Exp Psychol Learn Mem Cogn. 2018 Feb 01;:

Authors: Gilbert RA, Davis MH, Gaskell MG, Rodd JM

Abstract
Research has shown that adults' lexical-semantic representations are surprisingly malleable. For instance, the interpretation of ambiguous words (e.g., bark) is influenced by experience such that recently encountered meanings become more readily available (Rodd et al., 2016, 2013). However, the mechanism underlying this word-meaning priming effect remains unclear, and competing accounts make different predictions about the extent to which information about word meanings that is gained within one modality (e.g., speech) is transferred to the other modality (e.g., reading) to aid comprehension. In two Web-based experiments, ambiguous target words were primed with either written or spoken sentences that biased their interpretation toward a subordinate meaning, or were unprimed. About 20 min after the prime exposure, interpretation of these target words was tested by presenting them in either written or spoken form, using word association (Experiment 1, N = 78) and speeded semantic relatedness decisions (Experiment 2, N = 181). Both experiments replicated the auditory unimodal priming effect shown previously (Rodd et al., 2016, 2013) and revealed significant cross-modal priming: primed meanings were retrieved more frequently and swiftly across all primed conditions compared with the unprimed baseline. Furthermore, there were no reliable differences in priming levels between unimodal and cross-modal prime-test conditions. These results indicate that recent experience with ambiguous word meanings can bias the reader's or listener's later interpretation of these words in a modality-general way. We identify possible loci of this effect within the context of models of long-term priming and ambiguity resolution. (PsycINFO Database Record

PMID: 29389181 [PubMed - as supplied by publisher]

The effects of thematic relations on picture naming abilities across the lifespan.

Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2016 Jul;23(4):499-512

Authors: Hashimoto N, Johnson B, Peterson A

Abstract
A picture-word interference paradigm tracked patterns of activation during picture naming in 87 individuals (age range 17-80 years old). Distractor words were presented at stimulus onset asynchronies (SOAs) of -200, -100, and 0 ms bearing a has a-, location, or no relationship to the picture. Analyses of group naming reaction times revealed significant facilitation effects for both semantic relation types for all age groups. Analyses of temporal patterns of activation revealed significant effects primarily at SOAs of -200 and -100 ms. These findings provide evidence that both thematic relations are particularly salient in how semantic knowledge is organized, and that the patterns of effects from these semantic relations remain the same as one ages.

PMID: 26667786 [PubMed - indexed for MEDLINE]

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

PLoS One. 2018;13(1):e0191263

Authors: Bibault JE, Zapletal E, Rance B, Giraud P, Burgun A

Abstract
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).

PMID: 29351341 [PubMed - in process]

OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system.

J Biomed Semantics. 2018 Jan 18;9(1):5

Authors: Senderov V, Simov K, Franz N, Stoev P, Catapano T, Agosti D, Sautter G, Morris RA, Penev L

Abstract
BACKGROUND: The biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy.
RESULTS: OpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System. A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses.
CONCLUSIONS: OpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.

PMID: 29347997 [PubMed - in process]

MIRO: guidelines for minimum information for the reporting of an ontology.

J Biomed Semantics. 2018 Jan 18;9(1):6

Authors: Matentzoglu N, Malone J, Mungall C, Stevens R

Abstract
BACKGROUND: Creation and use of ontologies has become a mainstream activity in many disciplines, in particular, the biomedical domain. Ontology developers often disseminate information about these ontologies in peer-reviewed ontology description reports. There appears to be, however, a high degree of variability in the content of these reports. Often, important details are omitted such that it is difficult to gain a sufficient understanding of the ontology, its content and method of creation.
RESULTS: We propose the Minimum Information for Reporting an Ontology (MIRO) guidelines as a means to facilitate a higher degree of completeness and consistency between ontology documentation, including published papers, and ultimately a higher standard of report quality. A draft of the MIRO guidelines was circulated for public comment in the form of a questionnaire, and we subsequently collected 110 responses from ontology authors, developers, users and reviewers. We report on the feedback of this consultation, including comments on each guideline, and present our analysis on the relative importance of each MIRO information item. These results were used to update the MIRO guidelines, mainly by providing more detailed operational definitions of the individual items and assigning degrees of importance. Based on our revised version of MIRO, we conducted a review of 15 recently published ontology description reports from three important journals in the Semantic Web and Biomedical domain and analysed them for compliance with the MIRO guidelines. We found that only 41.38% of the information items were covered by the majority of the papers (and deemed important by the survey respondents) and a large number of important items are not covered at all, like those related to testing and versioning policies.
CONCLUSIONS: We believe that the community-reviewed MIRO guidelines can contribute to improving significantly the quality of ontology description reports and other documentation, in particular by increasing consistent reporting of important ontology features that are otherwise often neglected.

PMID: 29347969 [PubMed - in process]

BRIDG: a domain information model for translational and clinical protocol-driven research.

J Am Med Inform Assoc. 2017 Sep 01;24(5):882-890

Authors: Becnel LB, Hastak S, Ver Hoef W, Milius RP, Slack M, Wold D, Glickman ML, Brodsky B, Jaffe C, Kush R, Helton E

Abstract
Background: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data.
Objective: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG.
Methods: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models.
Results: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains.
Interpretation: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

PMID: 28339791 [PubMed - indexed for MEDLINE]

Structural priming can inform syntactic analyses of partially grammaticalized constructions.

Behav Brain Sci. 2017 Jan;40:e288

Authors: Francis EJ

Abstract
Branigan & Pickering (B&P) argue successfully that structural priming provides valuable information for developing psychologically plausible syntactic and semantic theories. I discuss how their approach can be used to help determine whether partially grammaticalized constructions that have undergone semantic change also have undergone syntactic reanalysis. I then consider cases in which evidence from priming cannot distinguish between competing syntactic analyses.

PMID: 29342713 [PubMed - in process]

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability.

J Biomed Semantics. 2018 Jan 12;9(1):3

Authors: He Y, Xiang Z, Zheng J, Lin Y, Overton JA, Ong E

Abstract
Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an "eXtensible Ontology Development" (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

PMID: 29329592 [PubMed - in process]

[Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service].

J Korean Acad Nurs. 2017 Dec;47(6):806-816

Authors: Kim M, Choi M, Youm Y

Abstract
PURPOSE: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.
METHODS: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.
RESULTS: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality.
CONCLUSION: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

PMID: 29326411 [PubMed - in process]

Biotea: semantics for Pubmed Central.

PeerJ. 2018;6:e4201

Authors: Garcia A, Lopez F, Garcia L, Giraldo O, Bucheli V, Dumontier M

Abstract
A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.

PMID: 29312824 [PubMed]

Inferring ontology graph structures using OWL reasoning.

BMC Bioinformatics. 2018 Jan 05;19(1):7

Authors: Rodríguez-García MÁ, Hoehndorf R

Abstract
BACKGROUND: Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge.
RESULTS: We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .
CONCLUSIONS: Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

PMID: 29304741 [PubMed - in process]

KQA: A Knowledge Quality Assessment Model for Clinical Decision Support Systems.

Stud Health Technol Inform. 2017;245:983-986

Authors: Zolhavarieh S, Parry D

Abstract
Informatics researchers have developed many methods for using computers to utilize knowledge in decision making in the form of clinical decision support systems (CDSSs). These systems can enhance human decision making in the healthcare domain. The knowledge acquisition bottleneck is one of the well-known issues in developing knowledge-based systems such as CDSS. It can be considered as a flow of knowledge from different knowledge sources to the main system. Most existing methods for extracting knowledge from knowledge resources suffer from the lack of a proper mechanism for extracting high-quality knowledge. In this paper, we propose a framework to discover high-quality knowledge by utilizing Semantic Web technologies.

PMID: 29295247 [PubMed - in process]

Semantic Web Service Delivery in Healthcare Based on Functional and Non-Functional Properties.

Stud Health Technol Inform. 2017;245:945-949

Authors: Schweitzer M, Gorfer T, Hörbst A

Abstract
In the past decades, a lot of endeavor has been made on the trans-institutional exchange of healthcare data through electronic health records (EHR) in order to obtain a lifelong, shared accessible health record of a patient. Besides basic information exchange, there is a growing need for Information and Communication Technology (ICT) to support the use of the collected health data in an individual, case-specific workflow-based manner. This paper presents the results on how workflows can be used to process data from electronic health records, following a semantic web service approach that enables automatic discovery, composition and invocation of suitable web services. Based on this solution, the user (physician) can define its needs from a domain-specific perspective, whereas the ICT-system fulfills those needs with modular web services. By involving also non-functional properties for the service selection, this approach is even more suitable for the dynamic medical domain.

PMID: 29295239 [PubMed - in process]

Open IoT Ecosystem for Enhanced Interoperability in Smart Cities-Example of Métropole De Lyon.

Sensors (Basel). 2017 Dec 08;17(12):

Authors: Robert J, Kubler S, Kolbe N, Cerioni A, Gastaud E, Främling K

Abstract
The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated "Intranets of Things", also referred to as "vertical silos", which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

PMID: 29292719 [PubMed - in process]