Resource Ontology Research Articles

PRESENTISM, TRUTH AND SUPERVENIENCE

AbstractTruthmaker theory is commonly thought to pose a challenge for presentism. Presentism seems to lack the ontological and ideological resources required to adequately underwrite the truth of propositions concerning the past. That is because if presentism is true, then the past does not exist. According to the standard response to this challenge, the truth of propositions concerning the past supervenes on surrogate entities that ‘stand proxy’ for past things. I argue that in order for the standard response to the truthmaker challenge to succeed these surrogate entities must stand in necessary connections to the past. I go on to argue that because the standard response is already committed to denying the existence of cross‐temporal modal connections of this kind, by its own lights that response is in error.1

FirstOnt: Automatic Construction of Ontologies out of Multiple Ontological Resources

Nowadays, a great amount of the contents of the WWW are still mainly only human-oriented. To progressively move into the Semantic Web, the adoption of the use of ontologies is a milestone. However, their elaboration from scratch is quite expensive, which could prevent non-experts from using them in their applications. Due to this reason, different approaches for ontology reusing and engineering have been proposed, but they require domain experts and knowledge engineers. Besides, finding an ontology (if it exists) that fits a specific domain can be a tedious and frustrating experience. In this paper, we propose an approach that, taking as starting point a set of user keywords that define the domain of interest, automatically integrates an ontology that comprises information extracted from different relevant resources. This ontology can be directly used as an initial version for a progressive refinement or as a final resource for non-expert users helping to spread the use of ontologies. This way, our proposal succeeds in alleviating the development efforts to create an ontology and facilitate this task to non-expert users. Moreover, it encourages ontology reuse and to reach a consensus when using these ontologies, for instance, to publish Linked Data.

A method for ontology construction derived from folksonomy

In this paper, we propose a comprehensive approach for deriving ontology from folksonomy. In our method, we mainly utilise spectral clustering algorithm (SCA) based on the concept of similarity between points instead of the distance. The details are as follows. Firstly, we filter tags that are wrong or non-English words. Secondly, overlap similarity is used to measure similarity between co-occurrence tags. And SCA is applied to cluster tags with semantic relations. Moreover, another algorithm is adopted to obtain hierarchical structure of tags by running SCA repeatedly. Finally, online ontology resource WordNet is applied to add definitions and other semantic relations between tags to finish the work. We also conduct an experiment in which we use the tags from delicious website as the external knowledge source for the domains of the tags. And the result shows that this method is appropriate for ontology construction of emerging areas due to that folksonomy covers the latest concepts.

Research of Information Integration System Supporting for SMEs Rapid Product Development

An information integration supporting system supporting for SMEs rapid product development is established. The architecture of the system based on web services is builded and the key technologies such as the building of information resources ontology model, the semantic retrieval and the selection method of information resource are discussed. The system supports enterprises for rapid product development and enables product development and personnel to make full use of network resources, enhance their ability to develop new products.

The impact of focused Gene Ontology curation of specific mammalian systems.

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome.AvailabilityGO annotation data is freely available from: ftp://ftp.geneontology.org/pub/go/gene-associations/

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource

In this paper, we present the design of ONDINE system which allows the loading and the querying of a data warehouse opened on the Web, guided by an Ontological and Terminological Resource (OTR). The data warehouse, composed of data tables extracted from Web documents, has been built to supplement existing local data sources. First, we present the main steps of our semiautomatic method to annotate data tables driven by an OTR. The output of this method is an XML/RDF data warehouse composed of XML documents representing data tables with their fuzzy RDF annotations. We then present our flexible querying system which allows the local data sources and the data warehouse to be simultaneously and uniformly queried, using the OTR. This system relies on SPARQL and allows approximate answers to be retrieved by comparing preferences expressed as fuzzy sets with fuzzy RDF annotations.

Disease Ontology: a backbone for disease semantic integration

The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.

Evaluation of pathways and new host proteins involved in CD4 down-modulation during HIV-1 infection

Down-modulation of the CD4 receptor expressed on T cells is one of the hallmarks of Human Immunodeficiency Virus (HIV) infection and it is believed to confer to the virus a selective replicative advantage in vivo. HIV has evolved redundant mechanisms to remove the receptor from the cell surface and accelerate its degradation. This process is mainly mediated by three viral proteins: Vpu, Env and Nef. Up to date, the mechanisms that lead to CD4 depletion from the surface of CD4+ T lymphocytes, the natural targets of HIV, are still poorly understood and only partially characterized. We are interested in the discovery of pathways and human proteins involved in the process, in order to eventually find potential new drug targets. To pursue our aim, we first performed a functional screening on HeLa CD4+ cells expressing Nef, using a shRNA lentiviral interference delivery system targeting the whole human genome compatible with an Affymetrix GeneChip Microarray. The read out was the rescue of the CD4 high phenotype, despite Nef expression. The results were analyzed with Affymetrix Expression Console 1.1 software and the web-based gene ontology resource DAVID. After four different screens we obtained a final list of 75 genes enriched in the cells sorted for high surface CD4. These genes appear to code for proteins involved in endocytic trafficking, trans-Golgi trafficking and lysosomal degradation pathways. We decided to validate our hits in primary blood CD4+ T cells, the most relevant for this kind of studies, and we set up a protocol for the transduction of these cells with lentiviral and retroviral vectors, necessary respectively for the expression of the shRN As and the viral proteins Vpu, Nef and Env. The actual down-regulation of the gene expression will be validated via quantitative Real Time PCR and Western Blot and the experiments will be eventually repeated in the context of HIV-1 infection.

Reconfigurable Manufacturing Resource Modeling Technology

How to model reconfigurable resources, how to make manufacturing resources to rapidly form into new production functions and capability is an unsolved problem. Based on current manufacturing resource knowledge representation, in the paper, manufacturing resource ontology modeling method is presented which has considered characteristics of reconfigurable manufacturing system (RMS), configuration changeable and system adjustable. Furthermore, resource description ontology is defined and example model is also presented; after formalize the concepts, attributes and relations of ontology, the knowledge base including ontology base, attribute base and relation base are constructed. The method proposed can provide strong support for reconfiguration and deformation of RMS.

NIF and NeuroLex

AbstractIn the digital age one can find data and teaching materials on the web, however data and materials of a high quality are not always ranked very high on commercial search engines that look at the number of other sites that link to a page rather than the quality of the information. Additionally, many such materials are maintained within databases that are inaccessible to search technology. To solve these problems, the Neuroscience Information Framework (NIF: http://www.neuinfo.org) was created to aid the neuroscience community to discover useful digital resources, such as academic databases, and it has also developed a large digital catalog of resources that are related to neuroscience.NIF has developed a “resource ontology”, and synchronized to a large extent the effort with the BRO (biomedical resource ontology) and assigned all 4000+ digital resources within the catalog to one or more of these ontological categories, making it possible to discover for example: all atlases through the NIF catalog. The resource categories are: Data, Funding, Job, Material, People, Services, Software, and Training. Most of these comprise sub-categorizations, definitions and synonyms and they can be viewed in the Neurolex found at http://neurolex.org/wiki/Resource_Type_Hierarchy. Each “resource descriptor” has been coded in OWL format files and has a unique identifier as well as synonyms and subclasses to help search systems locate data.Cataloging efforts of digital resources are tricky because unlike publications they can change at any time, including major shifts of data and structure, therefore the curation effort must be reasonably scoped and relatively nimble. NIF has developed some self reporting tools in the DISCO suite, accessible from http://disco.neuinfo.org/webportal/discoDashboardShow.do, to create just such a nimble resource identification system such that when a resource changes content or data structure significantly, the resource provider (such as the webmaster or database engineer) can change a small xml file sitting on their home page that will alert the NIF system and any other automated agent that the resource has changed. This tool in addition to the human curation effort results in a catalog that is reasonably representative of the state of biomedical resources.NIF maintains a list of current policies, issues and solicits input from community members at https://confluence.crbs.ucsd.edu/display/NIF/NIF+Resources+and+Curation+Policies.

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. witte@semanticsoftware.info.

OntoCAT - an integrated programming toolkit for common ontology application tasks

AbstractOntoCAT provides high level abstraction for interacting with ontology resources including local ontology files in standard OWL and OBO formats (via OWL API) and public ontology repositories: EBI Ontology Lookup Service (OLS) and NCBO BioPortal. 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.

Architecture and Resource Integration for P2P Semantic Manufacturing Grid

Manufacturing Grid aims at looking for an innovative next generation inter-organizational collaborative environment. A new architecture named P2P Semantic Manufacturing Grid (P2PSMG) was introduced in this research, which adopts ontology as a common language to help different participants to understand each other, and works in a semantic grid service oriented Napster style. The means of resource integration under this architecture was emphatically studied, by which service providers virtualize their manufacturing resources, encapsulate them into multi-granularity services based on resource ontology, and then submit their semantic descriptions to a third party registry for integration and discovery. A prototype system was developed, by which the feasibility of the means of resource integration introduced in this research is validated.

SU‐E‐T‐23: Ontological Representation of Radiation Treatment Guidelines

Purpose: To develop a Radiation Treatment Guidelines Ontology (RTGO) that represents the concepts, knowledge, and guidelines in radiation therapy using a formal and computable framework. This ontology will serve as the foundation of a decision support system for radiation oncologists. Method and Materials: The ontology framework chosen to represent RTGO is the Web Ontology Language (OWL) (www.w3.org/TR/owl‐guide). The OWL is rooted in the rigorous foundation of Description Logic and represents the latest development in ontology languages. The Protégé‐OWL ontology editor and knowledge‐base framework (http://protégé.stanford.edu) is used to build the ontology. The ontology is developed in a multi‐layered, modular, and iterative fashion. Upper ontologies are used to provide a sound structure. Concepts from standard ontologies are adopted whenever possible. Salient concepts and relationships are incorporated into the ontology through multiple iterations of extraction, modeling, implementation, and evaluation. Each iteration examines increasingly detailed concepts and relationships. The concepts are asserted in a strict subsumption hierarchy and are carefully formulated to be at a proper level of abstraction consistent with anticipated questions addressed by the ontology. Major ontology resources used in this project include National Center Biomedical Ontologyˈs Bioportal (bioportal.bioontology.org) and National Library of Medicineˈs Universal Medical Language System (UMLS) (www.nlm.nih.gov/research/umls). Major ontologies consulted include FMA for anatomical concepts, SNOMED for clinical findings and symptoms, and Semantic Network for relationships. Results: The initial focus is on modeling the concepts and knowledge summarized in the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) papers. Starting with the organ specific papers on bladder and rectum, the ontology has so far captured 171 concepts, 80 relationships, and 21 individuals. The development is proceeding to include other organs in the QUANTEC literature. Conclusion: Early results demonstrate that major concepts in QUANTEC papers regarding dose volume effects can be successfully captured in an ontological framework.Duke University has a Master Research Agreement with Varian Medical Systems, INC.

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

Combining Ontology Development Methodologies and Semantic Web Platforms for E-government Domain Ontology Development

One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protege and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protege and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing e-government developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations.

온톨로지 검색에 있어서 사용자 질의어와 온톨로지 리소스와의 상이성 해소를 위한 질의어 변환

차세대 웹 기술로 각광 받고 있는 시맨틱 웹에서 웹 리소스는 온톨로지에 기술된 다양한 메타데이타에 의해 가용하게 되며 이러한 온톨로지를 대상으로 한 검색을 위해 SPARQL과 같은 여러 시맨틱 질의 언어가 제안되었다. 그러나 이러한 시맨틱 질의 언어를 사용자가 습득하였다 하더라도 올바른 정보를 찾기 위해서는 사용자가 온톨로지의 구조와 어휘를 잘 파악하고 있어야 한다는 어려움이 있다. 특히 사용자가 질의 언어의 문법과 복잡한 형식 논리 이론에 익숙하더라도 온톨로지 리소스 표기에 사용된 어휘를 모를 경우에는 질의 언어 작성 시 전혀 다른 어휘를 사용할 수 있으며 그에 따라 원하는 정보를 찾지 못하는 어휘 상이성 문제를 해결하여야 한다. 본 논문은 이러한 어휘 상이성 문제의 해소에 주안점을 두고 질의어의 의미와 온톨로지 어휘 사이의 의미 유사도를 이용하여 사용자가 온톨로지에 없는 질의어를 사용하였을 경우 그와 의미가 가장 가까운 온톨로지 어휘로 변환하여 줌으로써 어휘 상이성 문제를 해결하는 방안을 제안한다. Ontologies are playing an important role in semantic web which is emerging as a next stage of the web revolution because various kinds of metadata are described in ontologies. Correspondingly, many query languages like SPARQL, RDQL etc. have been proposed for querying these ontologies. But users have to know the structures and resource names of ontologies completely to get search results even if they have expertise on complex formal logic and syntax of the query languages. Especially, casual users do not know the resource names and may use different words from resource names when they write their query language. This vocabulary gap problem have to be solved to raise the success rate. In this paper, an approach for translating user's search words to corresponding resource names has been proposed. This approach uses semantic similarity between user created search words and ontology resource names.

Search Engine System Based on Ontology of Technological Resources

Internet has become a huge and updating information warehouse, and provides a new source for us to build a well technological resources sharing system to support our research work and development activities. However, the technological resources on Internet is usually diverse, professional and complex. They are difficult to be retrieved precisely and completely by traditional search engines. This paper proposed a new search engine system based on ontology of technological resources. In that system, a database with ontology knowledge warehouse was designed to store all related conceptions and the relationships of technological domains. By semantic analysis of users' queries and a heuristic search, the expected technological resources can be retrieved more precisely and completely to satisfy their intentions.

ArabOnto: experimenting a new distributional approach for building Arabic ontological resources

Ontologies are useful for modelling and retrieving knowledge in complex information systems. Ontology construction environments use statistical and linguistic information to extract knowledge from corpora. Within the great improvement in this field, there is a need to introduce the Arabic language in these environments. We present the ArabOnto architecture modelling the process of Arabic ontology extraction from corpora. ArabOnto focuses on linguistic issues related to Arabic term extraction and linking (i.e. from morphosyntactic parsing to clustering). We experiment our system by testing several alternatives on three domains. Besides, our ontologies are validated in the context of an information retrieval system.