Ontology Content Research Articles

ВИКОРИСТАННЯ ОНТОЛОГІЙ НАВЧАЛЬНОГО ПРИЗНАЧЕННЯ ДЛЯ ФОРМУВАННЯ НАВЧАЛЬНО-ОПЕРАЦІЙНОГО СЕРЕДОВИЩА

Розглянуто питання ефективного пошуку і використання навчальних матеріалів усистемах електронного навчання. Показано, що одним із варіантів вирішення цього питанняє використання онтологій та засобів семантичної мережі при проектуванні засобівнавчання.Ключові слова: ергономічні особливості, засоби подання знань, інформаційний простір,навчальне середовище, онтологія, предметна область.Ключові слова: ергономічні особливості, засоби подання знань, інформаційний простір,навчальне середовище, онтологія, предметна область.

BCIO: Brain Computer Interface Ontology

Brain Computer Interface (BCI) enable paralyze peoples to interact and control their environment by defining the direct communication between human feelings (brain) and technological aspects (external device). The feature extraction and translation of commands in BCI are the critical tasks, which are the key of BCI system. Using the semantic method will enhance the both of these features. Nowadays ontology for representing knowledge is becoming more popular in researchers to describe, share and integrate their scientific data. If the information, algorithms and results are stored in the form of ontological content, it will provide the efficient way to use and reuse the data related to BCI and will help to BCI researchers for standardization. Adding semantic in BCI will also improve the efficiency of current BCI system. General Terms Human Factor, Brain Computer Interface.

An ontology for managing network services quality

The evolution of IP networks to a service-oriented paradigm poses new challenges to service providers regarding the management and auditing of network services. The upward trend in ubiquity, heterogeneity and virtualization of network services and resources demands for a formal and systematic approach to network management tasks.In this context, the semantic characterization and modeling of services provided to users is a key component to sustain autonomic service management, service negotiation and configuration. The semantic and formal description of services and resources is also relevant to assist paradigms such as cloud computing, where a large diversity of resources have to be described and managed in a highly dynamic way.This paper defines an ontology for multiservice IP networks targeting multiple service management goals, namely: (i) to foster client and service provider interoperability; (ii) to manage network service contracts, promoting the dynamic negotiation between parties; (iii) to access and query SLA/SLSs data on a individual or aggregated basis to assist service provisioning in the network; and (iv) to sustain service monitoring and auditing. A ServiceModel API is provided to take full advantage of the proposed semantic model, allowing Service Management Platforms to access the ontological contents. This ontological development takes advantage of SWRL to discover new knowledge, enriching the possibilities of systems described using this support.

The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation

The knowledge acquisition (KA) process is not “mining from the expert’s head” and writing rules for building knowledge-based systems (KBS), as it was 20years ago when KA was often confused with knowledge elicitation activity, and modern engineering tools did not exist. The KA process has definitely changed. Today knowledge acquisition is considered a cognitive process that involves both dynamic modeling and knowledge generation activities. KA should be seen as a spiral of epistemological and ontological content that grows upward by transforming tacit knowledge into explicit knowledge, which in turn becomes the basis for a new spiral of knowledge generation. This paper presents some of our attempts to build a new knowledge acquisition methodology that brings together and includes all of these ideas. KAMET II, the evolution of KAMET (Cairó, 1998), represents a modern approach to creating diagnosis-specialized knowledge models that can be run by Protégé 2000, the open source ontology editor and knowledge-based framework.

Populous: a tool for building OWL ontologies from templates.

BackgroundOntologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists.ResultsWe present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists.ConclusionsPopulous's contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.

The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications.

The semantic web depends on the use of ontologies to let electronic systems interpret contextual information. Optimally, the handling and access of ontologies should be completely transparent to the user. As a means to this end, we have developed a service that attempts to bridge the gap between experts in a certain knowledge domain, ontologists, and application developers. The ontology-based answers (OBA) service introduced here can be embedded into custom applications to grant access to the classes of ontologies and their relations as most important structural features as well as to information encoded in the relations between ontology classes. Thus computational biologists can benefit from ontologies without detailed knowledge about the respective ontology. The content of ontologies is mapped to a graph of connected objects which is compatible to the object-oriented programming style in Java. Semantic functions implement knowledge about the complex semantics of an ontology beyond the class hierarchy and “partOf” relations. By using these OBA functions an application can, for example, provide a semantic search function, or (in the examples outlined) map an anatomical structure to the organs it belongs to. The semantic functions relieve the application developer from the necessity of acquiring in-depth knowledge about the semantics and curation guidelines of the used ontologies by implementing the required knowledge. The architecture of the OBA service encapsulates the logic to process ontologies in order to achieve a separation from the application logic. A public server with the current plugins is available and can be used with the provided connector in a custom application in scenarios analogous to the presented use cases. The server and the client are freely available if a project requires the use of custom plugins or non-public ontologies. The OBA service and further documentation is available at http://www.bioinf.med.uni-goettingen.de/projects/oba

Perspectives of Semantic Web in E Commerce

The Semantic Web Technologies growing interest in human computer interactions where it enables the machines to interpret the data published in a machine interpretable form under web. Currently, E-Commerce is seriously vulnerable due to the lack of proper standards where HTML does not provide the syntax and semantics of information. Semantic Web has the potential to extremely influence the further development of the internet market, where e-commerce plays an important role. This paper discusses the semantic architecture for e-commerce using ontology language like RDF. This gives the overview about how the producer describes their resource in ontology content and how the consumer will retrieve the efficient data.

BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications

The National Center for Biomedical Ontology (NCBO) is one of the National Centers for Biomedical Computing funded under the NIH Roadmap Initiative. Contributing to the national computing infrastructure, NCBO has developed BioPortal, a web portal that provides access to a library of biomedical ontologies and terminologies (http://bioportal.bioontology.org) via the NCBO Web services. BioPortal enables community participation in the evaluation and evolution of ontology content by providing features to add mappings between terms, to add comments linked to specific ontology terms and to provide ontology reviews. The NCBO Web services (http://www.bioontology.org/wiki/index.php/NCBO_REST_services) enable this functionality and provide a uniform mechanism to access ontologies from a variety of knowledge representation formats, such as Web Ontology Language (OWL) and Open Biological and Biomedical Ontologies (OBO) format. The Web services provide multi-layered access to the ontology content, from getting all terms in an ontology to retrieving metadata about a term. Users can easily incorporate the NCBO Web services into software applications to generate semantically aware applications and to facilitate structured data collection.

OntoPlus: Text-driven ontology extension using ontology content, structure and co-occurrence information

This paper addresses the process of semi-automatic text-driven ontology extension using ontology content, structure and co-occurrence information. A novel OntoPlus methodology is proposed for semi-automatic ontology extension based on text mining methods. It allows for the effective extension of the large ontologies, providing a ranked list of potentially relevant concepts and relationships given a new concept (e.g., glossary term) to be inserted in the ontology. A number of experiments are conducted, evaluating measures for ranking correspondence between existing ontology concepts and new domain concepts suggested for the ontology extension. Measures for ranking are based on incorporating ontology content, structure and co-occurrence information. The experiments are performed using a well known Cyc ontology and textual material from two domains – finances and, fisheries & aquaculture. Our experiments show that the best results are achieved by combining content, structure and co-occurrence information. Furthermore, ontology content and structure seem to be more important than co-occurrence for our data in the financial domain. At the same time, ontology content and co-occurrence seem to have higher importance for our fisheries & aquaculture domain.

OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript

BackgroundOntologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups.ResultsOntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application.ConclusionsOntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases.Availabilityhttp://www.ontocat.org

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases.Database URL: http:www.PhenomicsWorld.org/QBTA.php

Six in one hand, half a dozen in the other: An ontological content analysis of radical Islam and the Christian right

The purpose of this paper is to examine the logics of justification embedded in the discourse of radical Islam and fundamentalist Christianity. Proponents of either side mostly suggest that their ideological system stands in opposition to the other. But such opposition holds only for the substantive content of these religious ideologies, not necessarily their underlying logics. That is, they indeed worship different deities, practice different customs and rituals, and in many respects, see the world in very different ways. However, our analysis examines the logical construction of their respective ideological discursive justifications, finding that differences in content are nonetheless underpinned by a remarkably similar system of logic. Previous comment on underlying similarities between these religions has been based on a sense of their affective tenor, observations of their similar rhetoric and methods of activism and otherwise random fragments of coincidence. We instead apply an ontological schema to paradigmatic discursive examples of each, thereby illustrating that their core logics are fundamentally the same.

Mapping between the OBO and OWL ontology languages

BackgroundOntologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL.ResultsWe have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source.ConclusionsOur transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.

Populous: A Tool For Populating OWL Ontologies From Templates

AbstractWe present Populous, a tool for gathering content with which to populate an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity attribute.Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. Populous's contribution is in the knowledge gathering stage of ontology development. It separates knowledge gathering from the conceptualisation and also separates the user from the standard ontology authoring environments. As a result, Populous can allow knowledge to be gathered in a straight-forward manner that can then be used to do mass production of ontology content.

GOVis, A Gene Ontology Visualization Tool Based on Multi-Dimensional Values

Most of gene product similarity measurements concentrate on the information content of Gene Ontology (GO) terms or use a path-based similarity between GO terms, which may ignore other important information contained in the structure of the ontology. In our study, we integrate different GO similarity measure approaches to analyze the functional relationship of genes and gene products with a new triangle-based visualization tool called GO(Vis). The purpose of this tool is to demonstrate the effect of three important information factors when measuring the similarity between gene products. One advantage of this tool is that its important ratio can be adjusted to meet different measuring requirements according to the biological knowledge of each factor. The experimental results demonstrate that GO(Vis) can display diagrams of the functional relationship for gene products effectively.

A proposal to evaluate ontology content

Research into ontologies has received much attention and has been the subject of study in different scientific communities. The current growth observed in ontology use and availability increases th...

BioPortal: ontologies and integrated data resources at the click of a mouse

Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.

A proposal to evaluate ontology content

Research into ontologies has received much attention and has been the subject of study in different scientific communities. The current growth observed in ontology use and availability increases the importance of the evolution of methods to evaluate ontologies. There are formal methodologies available, but the evaluation of ontology content does not seem to be a main concern. We understand the evaluation of ontology content to be the activity of verifying whether the ontology really represents the knowledge of a domain, according to the assessment of experts. Our objective is to contribute to this sense. In order to do so, we present qualitative research about the content evaluation of an organizational ontology, developed within a large Brazilian energy utility company. The evaluation process consists of a set of questionnaires, which are based on a multidisciplinary approach, and of a prototype system. We believe that this proposal can contribute towards improvements in both modeling and evaluation processes in ontology development.

NeuroLex.org - A semantic wiki for neuroinformatics based on the NIF Standard Ontology

Event Abstract Back to Event NeuroLex.org - A semantic wiki for neuroinformatics based on the NIF Standard Ontology Stephen Larson1*, Sarah Maynard1, Fahim Imam1 and Maryann Martone1 1 UCSD, Department of Neuroscience, Faculty of Health,Medicine and Life Sciences, United States The neuroscience community needs its basic domain concepts organized into a coherent framework. Ontologies provide an important medium for reconciling knowledge into a portable and machine readable form. For many years we have been building community ontologies for neuroscience, first through the Biomedical Informatics Research Network and now through the Neuroscience Information Framework projects (http://neuinfo.org). These projects resulted in the construction of a large modular ontology, constructed by importing existing ontologies where possible, called the NIF Standard Ontology (NIFSTD; Bug et al, 2008). The list of topics it covers includes behavioral activity, behavioral paradigms, brain regions, cells, diseases, molecules, nervous system function, subcellular components, information resources, resource types, and qualities.One of the largest roadblocks that we encountered was the lack of tools for domain experts to view, edit, and contribute their knowledge to the NIFSTD ontology. Existing editing tools were difficult to use or required expert knowledge to employ. By combining several open source technologies related to semantic wikis and NIFSTD, we have created NeuroLex.org, the first semantic wiki for neuroscience. NeuroLex.org is built on top of the open source Semantic Mediawiki platform (http://semantic-mediawiki.org). Semantic Mediawiki makes querying the concepts in the ontology via properties or class hierarchy very straightforward. In addition, we have incorporated tools such as Semantic Forms, which allow the ontology classes to be edited as a form rather than as a wiki page with special text mark-up. Some of the fields in the form support autocomplete which allows users to populate those fields with other classes from the ontology. NeuroLex.org has evolved into a powerful platform for collaboratively maintaining and extending the NIFSTD ontology. We have been able to incorporate user feedback and create custom views of the ontology content with very rapid turnaround. Table 1 shows some key metrics that we have collected on its usage. The content contributed to the Neurolex is not directly added to NIFSTD, but is incorporated into the NIFSTD OWL file by a knowledge engineer after curation by the NIF ontology group. We conclude that the Semantic Mediawiki is a good starting point for the collaborative maintenance of ontologies. Other groups are also using a similar approach, e.g., BioMedGT (http://biomedgt.nci.nih.gov). While we are still working through some issues, e.g., synchronizing the NIFSTD with the content on NeuroLex, exporting and importing OWL, and bulk uploading concepts, we believe that semantic wikis are a good tool for providing community contribution and feedback to projects like NIF.

Implementation of an onto-wiki toolkit using web services to improve the efficiency and effectiveness of medical ontology co-authoring and analysis

The ICD11 draft was launched by the WHO in order to define ICD ontology by selected experts using a wiki-like structured joint-authoring tool. The challenge of this expert-/community-based ontology co-authoring is how to manage and process the ontology objects in the wiki page. The wiki-tools in the market require lots of time and human effort to organise, process and extract the ontology content from the wiki page for review and analysis. Therefore, this article is to investigate how to apply semantic web technology to improve the efficiency and effectiveness of ontology co-authoring and analysis. An onto-wiki toolkit that provides a set of web services is proposed for ontology creators to create, co-edit, organise, map and relate the wiki-like structured ontology objects on a wiki page for review and analysis. By using the onto-wiki toolkits, the pace of terminology standardisation, e-patient records integration, exchange and sharing can be improved.