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Evaluating Scientific Domain Ontologies for the Electromagnetic Knowledge Domain: A General Methodology  [PDF]
Alessandra Esposito,Marco Zappatore,Luciano Tarricone
International Journal of Web & Semantic Technology , 2011,
Abstract: The adoption of ontologies as a formalized approach to information codification is a constant-growing phenomenon in scientific research. Moreover, knowledge sharing and reuse can be improved by adopting hierarchical and modular frameworks and therein embedding available ontologies. Unfortunately, mergingprocedures may bring about severe, time-consuming problems if a careful selection process is not carried out. Based on these considerations, we propose a methodology for evaluating and selecting higher-levelontologies, given the lower-level ones. Our proposal is based on the computation of ad-hoc metrics which take into account structural and semantic aspects and on the adoption of a multi-decisional analysis procedure. The methodology has been applied to identify ontologies suitable for providing a scientificdomain high-level codification to Electromagnetism. Two well-known scientific domain ontologies have been selected and evaluated with the proposed methodology.
Turning Text into Research Networks: Information Retrieval and Computational Ontologies in the Creation of Scientific Databases  [PDF]
Flávio Ceci, Ricardo Pietrobon, Alexandre Leopoldo Gon?alves
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0027499
Abstract: Background Web-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive. Method We focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology. Results We present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances. Conclusion We have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.
Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies
Robert Hoehndorf, Frank Loebe, Janet Kelso, Heinrich Herre
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-377
Abstract: We have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach.Biomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.As the volume of biomedical data and knowledge presented in scientific papers increases, there is an increasing need to support formal analyses of these data and to pre-process knowledge for further use in solving problems and developing and testing hypotheses. The precise capture of biological data and knowledge and their correct and consistent representation in computational form is a basic pre-requisite for achieving these goals. Ontologies may provide a basis for integrating, processing and applying biomedical data. Their integration into a common ontological framework is an indispensible step towards the development of expressive knowledge bases. Interoperability between these ontologies would facilitate the consistent use of biomedical data in the form of annotations, allow for queries over multiple ontologies and form a rich knowledge resource for biomedicine that could be further used in solving problems and stating hypotheses. Different ontologies have been developed by different groups with different intentions. As a result, translating a statement or transferring an a
Ontologies for Bioinformatics
Nadine Schuurman,Agnieszka Leszczynski
Bioinformatics and Biology Insights , 2008,
Abstract: The past twenty years have witnessed an explosion of biological data in diverse database formats governed by heterogeneous infrastructures. Not only are semantics (attribute terms) different in meaning across databases, but their organization varies widely. Ontologies are a concept imported from computing science to describe different conceptual frameworks that guide the collection, organization and publication of biological data. An ontology is similar to a paradigm but has very strict implications for formatting and meaning in a computational context. The use of ontologies is a means of communicating and resolving semantic and organizational differences between biological databases in order to enhance their integration. The purpose of interoperability (or sharing between divergent storage and semantic protocols) is to allow scientists from around the world to share and communicate with each other. This paper describes the rapid accumulation of biological data, its various organizational structures, and the role that ontologies play in interoperability.
Benchmarking Ontologies: Bigger or Better?  [PDF]
Lixia Yao,Anna Divoli,Ilya Mayzus,James A. Evans,Andrey Rzhetsky
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1001055
Abstract: A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.
The category of networks of ontologies  [PDF]
Jér?me Euzenat
Computer Science , 2014,
Abstract: The semantic web has led to the deployment of ontologies on the web connected through various relations and, in particular, alignments of their vocabularies. There exists several semantics for alignments which make difficult interoperation between different interpretation of networks of ontologies. Here we present an abstraction of these semantics which allows for defining the notions of closure and consistency for networks of ontologies independently from the precise semantics. We also show that networks of ontologies with specific notions of morphisms define categories of networks of ontologies.
Didactical Ontologies
Steffen Mencke, Reiner Dumke,Reiner Dumke
International Journal of Emerging Technologies in Learning (iJET) , 2008,
Abstract: Ontologies are a fundamental concept of theSemantic Web envisioned by Tim Berners-Lee [1]. Togetherwith explicit representation of the semantics of data formachine-accessibility such domain theories are the basis forintelligent next generation applications for the web andother areas of interest [2]. Their application for specialaspects within the domain of e-learning is often proposed tosupport the increasing complexity ([3], [4], [5], [6]). So theycan provide a better support for course generation orlearning scenario description [7]. By the modeling ofdidactics-related expertise and their provision for thecreators of courses many improvements like reuse, rapiddevelopment and of course increased learning performancebecome possible due to the separation from other aspects ofe-learning platforms as already proposed in [8].
Multi-Connected Ontologies  [PDF]
Philip Davies,David Newell,Abigail Davies,Damla Karagozlu
Computer Science , 2011,
Abstract: Ontologies have been used for the purpose of bringing system and consistency to subject and knowledge areas. We present a criticism of the present mathematical structure of ontologies and indicate that they are not sufficient in their present form to represent the many different valid expressions of a subject knowledge domain. We propose an alternative structure for ontologies based on a richer multi connected complex network which contains the present ontology structure as a projection. We demonstrate how this new multi connected ontology should be represented as an asymmetric probability matrix.
Ontologies for the Description of Mouse Phenotypes  [PDF]
G. V. Gkoutos,E. C. J. Green,A.-M. Mallon,A. Blake,S. Greenaway,J. M. Hancock,D. Davidson
Comparative and Functional Genomics , 2004, DOI: 10.1002/cfg.430
Abstract: Ontologies are becoming increasingly important for the efficient storage, retrieval and mining of biological data. The description of phenotypes using ontologies is a particularly complex problem. We outline a schema that can be used to describe phenotypes by combining orthologous axiomatic ontologies. We also describe tools for storing, browsing and searching such complex ontologies. Central to this approach is that assays (protocols for measuring phenotypic characters) describe what has been measured as well as how this was done, allowing assays to link individual organisms to ontologies describing phenotypes. We have evaluated this approach by automatically annotating data on 600 000 mutant mice phenotypes using the SHIRPA protocol. We believe this approach will enable the flexible, extensible and detailed description of phenotypes from any organism.
OLSVis: an animated, interactive visual browser for bio-ontologies
Steven Vercruysse, Aravind Venkatesan, Martin Kuiper
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-116
Abstract: We created the OLSVis web application, a visualiser for browsing all ontologies available in the OLS database. OLSVis shows customisable subgraphs of the OLS ontologies. Subgraphs are animated via a real-time force-based layout algorithm which is fully interactive: each time the user makes a change, e.g. browsing to a new term, hiding, adding, or dragging terms, the algorithm performs smooth and only essential reorganisations of the graph. This assures an optimal viewing experience, because subsequent screen layouts are not grossly altered, and users can easily navigate through the graph. URL: http://ols.wordvis.com webciteThe OLSVis web application provides a user-friendly tool to visualise ontologies from the OLS repository. It broadens the possibilities to investigate and select ontology subgraphs through a smooth visualisation method.Ontologies constitute an increasingly important knowledge resource. In the biomedical domain the engineering of ontologies is predominantly organised by the Open Biomedical Ontology (OBO) Foundry [1]. Ontologies arrange terms hierarchically, connected by relationships in directed acyclic graphs. OBO ontologies represent formalised biological knowledge and are broadly used in the analysis and interpretation of experimental results, e.g. by linking Gene Ontology (GO) terms [2] to gene sets [3,4]. Ontologies provide also an important resource to find accurate terms for use in scientific reports.Many tools are available for browsing ontologies (see [5,6]). Several of them are integrated in systems dedicated to analyse specific data sets (e.g. calculating overrepresented GO categories in a gene list: GOrilla [7], agriGO [8], and GOTermFinder [3]). Other tools are designed for more general-purpose ontology exploration, such as QuickGO [9], AmiGO [10], or NCBO’s FlexViz [11]. Some of these ontology viewers are text-based, i.e. they use a folder/subfolder-interface to explore hierarchies (e.g. AmiGO [10], MGI GO Browser [12]). However, many
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