8,739 research outputs found
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
MultiFarm: A benchmark for multilingual ontology matching
In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual
ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different
languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism
Recommended from our members
Ontology mapping for semantically enabled applications
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services
A framework for deriving semantic web services
Web service-based development represents an emerging approach for the development of distributed information systems. Web services have been mainly applied by software practitioners as a means to modularize system functionality that can be offered across a network (e.g., intranet and/or the Internet). Although web services have been
predominantly developed as a technical solution for integrating software systems, there is a more business-oriented aspect that developers and enterprises need to deal with in order to benefit from the full potential of web services in an electronic market. This ‘ignored’ aspect is the representation of the semantics underlying the services themselves as well as the ‘things’ that the services manage. Currently languages like the Web Services Description Language (WSDL) provide the syntactic means to describe web services, but
lack in providing a semantic underpinning. In order to harvest all the benefits of web services technology, a framework has been developed for deriving business semantics from syntactic descriptions of web services. The benefits of such a framework are two-fold. Firstly, the framework provides a way to gradually construct domain ontologies from previously defined technical services. Secondly, the framework enables the
migration of syntactically defined web services toward semantic web services. The study follows a design research approach which (1) identifies the problem area and its relevance from an industrial case study and previous research, (2) develops the
framework as a design artifact and (3) evaluates the application of the framework through a relevant scenario
Behavior change interventions: the potential of ontologies for advancing science and practice
A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science
Drawing OWL 2 ontologies with Eddy the editor
In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments.
This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification
Recommended from our members
OntoEng: A design method for ontology engineering in information systems
This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the
gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design
method for ontology engineering
Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts
Introduction
The secondary use of Electronic Healthcare Records (EHRs) often requires the identification
of patient cohorts. In this context, an important problem is the heterogeneity of clinical data
sources, which can be overcome with the combined use of standardized information models,
Virtual Health Records, and semantic technologies, since each of them contributes to solving
aspects related to the semantic interoperability of EHR data. Our main objective is to develop
methods allowing for a direct use of EHR data for the identification of patient cohorts
leveraging current EHR standards and semantic web technologies.
Materials and Methods
We propose to take advantage of the best features of working with EHR standards and
ontologies. Our proposal is based on our previous results and experience working with both
technological infrastructures. Our main principle is to perform each activity at the abstraction
level with the most appropriate technology available. This means that part of the processing
will be performed using archetypes (i.e., data level) and the rest using ontologies (i.e.,
knowledge level). Our approach will start working with EHR data in proprietary format,
which will be first normalized and elaborated using EHR standards and then transformed into
a semantic representation, which will be exploited by automated reasoning.
Results
We have applied our approach to protocols for colorectal cancer screening. The results
comprise the archetypes, ontologies and datasets developed for the standardization and
semantic analysis of EHR data. Anonymized real data has been used and the patients have
been successfully classified by the risk of developing colorectal cancer.
Conclusion
This work provides new insights in how archetypes and ontologies can be effectively
combined for EHR-driven phenotyping. The methodological approach can be applied to other
problems provided that suitable archetypes, ontologies and classification rules can be
designed.This work was supported by the Ministerio de Economia y Competitividad and the FEDER program through grants TIN2010-21388-C01 and TIN2010-21388-C02. MCLG was supported by the Fundacion Seneca through grant 15555/FPI/2010.Fernández-Breis, JT.; Maldonado Segura, JA.; Marcos, M.; Legaz-García, MDC.; Moner Cano, D.; Torres-Sospedra, J.; Esteban-Gil, A.... (2013). Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. Journal of the American Medical Informatics Association. 20(E2):288-296. https://doi.org/10.1136/amiajnl-2013-001923S28829620E2Cuggia, M., Besana, P., & Glasspool, D. (2011). Comparing semi-automatic systems for recruitment of patients to clinical trials. International Journal of Medical Informatics, 80(6), 371-388. doi:10.1016/j.ijmedinf.2011.02.003Sujansky, W. (2001). Heterogeneous Database Integration in Biomedicine. Journal of Biomedical Informatics, 34(4), 285-298. doi:10.1006/jbin.2001.1024Schadow G Russler DC Mead CN . Integrating medical information and knowledge in the HL7 RIM. Proceedings of the AMIA Symposium, 2000:764–8.Johnson PD Tu SW Musen MA . A virtual medical record for guideline-based decision support. Proceedings of the AMIA 2001 Annual Symposium, 294–8.German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003Maldonado, J. A., Costa, C. M., Moner, D., Menárguez-Tortosa, M., Boscá, D., Miñarro Giménez, J. A., … Robles, M. (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics, 45(4), 746-762. doi:10.1016/j.jbi.2011.11.004Parker CG Rocha RA Campbell JR . Detailed clinical models for sharable, executable guidelines. Stud Health Technol Inform 2004;107:145–8.Clinical Information Modeling Initiative. http://informatics.mayo.edu/CIMI/index.php/Main_Page (accessed Jun 2013).W3C, OWL2 Web Ontology Language. http://www.w3.org/TR/owl2-overview/ (accessed Jun 2013).European Commission. Semantic interoperability for better health and safer healthcare. Deployment and research roadmap for Europe. ISBN-13: 978-92-79-11139-6, 2009.SemanticHealthNet. http://www.semantichealthnet.eu/ (accessed Jun 2013).Martínez-Costa, C., Menárguez-Tortosa, M., Fernández-Breis, J. T., & Maldonado, J. A. (2009). A model-driven approach for representing clinical archetypes for Semantic Web environments. Journal of Biomedical Informatics, 42(1), 150-164. doi:10.1016/j.jbi.2008.05.005Iqbal AM . An OWL-DL ontology for the HL7 reference information model. Toward useful services for elderly and people with disabilities Berlin: Springer, 2011:168–75.Tao, C., Jiang, G., Oniki, T. A., Freimuth, R. R., Zhu, Q., Sharma, D., … Chute, C. G. (2012). A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data. Journal of the American Medical Informatics Association, 20(3), 554-562. doi:10.1136/amiajnl-2012-001326Heymans, S., McKennirey, M., & Phillips, J. (2011). Semantic validation of the use of SNOMED CT in HL7 clinical documents. Journal of Biomedical Semantics, 2(1), 2. doi:10.1186/2041-1480-2-2Menárguez-Tortosa, M., & Fernández-Breis, J. T. (2013). OWL-based reasoning methods for validating archetypes. Journal of Biomedical Informatics, 46(2), 304-317. doi:10.1016/j.jbi.2012.11.009Lezcano, L., Sicilia, M.-A., & Rodríguez-Solano, C. (2011). Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules. Journal of Biomedical Informatics, 44(2), 343-353. doi:10.1016/j.jbi.2010.11.005Tao C Wongsuphasawat K Clark K . Towards event sequence representation, reasoning and visualization for EHR data. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI'12). New York, NY, USA: ACM:801–6.Bodenreider O . Biomedical ontologies in action: role in knowledge management, data integration and decision support. IMIA Yearbook of Medical Informatics 2008;67–79.Beale T . Archetypes. Constraint-based domain models for future-proof information systems. http://www.openehr.org/files/publications/archetypes/archetypes_beale_web_2000.pdfSNOMED-CT. http://www.ihtsdo.org/snomed-ct/ (accessed Jun 2013).UMLS Terminology Services. https://uts.nlm.nih.gov/home.html (accessed Jun 2013).The openEHR Foundation, openEHR Clinical Knowledge Manager. http://www.openehr.org/knowledge/ (accessed Jun 2013).Maldonado, J. A., Moner, D., Boscá, D., Fernández-Breis, J. T., Angulo, C., & Robles, M. (2009). LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics. International Journal of Medical Informatics, 78(8), 559-570. doi:10.1016/j.ijmedinf.2009.03.006SAXON XSLT and XQuery processor. http://saxon.sourceforge.net/ (accessed Jun 2013).NCBO Bioportal. http://bioportal.bioontology.org/ (accessed Jun 2013).The Protégé Ontology Editor and Knowledge Acquisition System. http://protege.stanford.edu/ (accessed Jun 2013).Semantic Web Integration Tool. http://sele.inf.um.es/swit (accessed Jun 2013).Hermit Reasoner. http://www.hermit-reasoner.com/ (accessed Jun 2013).The OWLAPI. http://owlapi.sourceforge.net/ (accessed Jun 2013).Institute for Health Metrics and Evaluation. Global Burden of Disease. http://www.healthmetricsandevaluation.org/gbd (accessed Jun 2013).Segnan N Patnick J von Karsa L . European guidelines for quality assurance in colorectal cancer screening and diagnosis 2010. First Edition. European Union. ISBN 978-92-79-16435-4.W3C. XQuery 1.0: An XML Query Language. http://www.w3.org/TR/xquery/ (accessed Jun 2013).DL Query. http://protegewiki.stanford.edu/wiki/DL_Query (accessed Jun 2013).SPARQL Query Language for RDF. http://www.w3.org/TR/rdf-sparql-query/ (accessed Jun 2013).Semantic Web Rule Language. http://www.w3.org/Submission/SWRL/ (accessed Jun 2013).Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Moner, D., Boscá, D., & Robles, M. (2011). An Archetype-Based Solution for the Interoperability of Computerised Guidelines and Electronic Health Records. Lecture Notes in Computer Science, 276-285. doi:10.1007/978-3-642-22218-4_35MobiGuide: Guiding patients anytime everywhere. http://www.mobiguide-project.eu/ (accessed Jun 2013).EURECA: Enabling information re-Use by linking clinical RE search and Care. http://eurecaproject.eu/ (accessed Jun 2013).Rea, S., Pathak, J., Savova, G., Oniki, T. A., Westberg, L., Beebe, C. E., … Chute, C. G. (2012). Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. Journal of Biomedical Informatics, 45(4), 763-771. doi:10.1016/j.jbi.2012.01.009Clinical Element Models. http://informatics.mayo.edu/sharp/index.php/CEMS (accessed Jun 2013)
A Large Scale Dataset for the Evaluation of Ontology Matching Systems
Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)
- …