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Author

Carlo Torniai

Bio: Carlo Torniai is an academic researcher from Oregon Health & Science University. The author has contributed to research in topics: Ontology (information science) & Semantic Web. The author has an hindex of 17, co-authored 49 publications receiving 1328 citations. Previous affiliations of Carlo Torniai include University of Southern California & University of Florence.

Papers
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Journal ArticleDOI
TL;DR: Uberon is presented, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria, allowing integration of model organism and human data.
Abstract: We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org

585 citations

Journal ArticleDOI
29 Apr 2016-PLOS ONE
TL;DR: The state of OBI and several applications that are using it are described, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources.
Abstract: The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

265 citations

Proceedings ArticleDOI
17 Sep 2007
TL;DR: Multimedia ontologies, that include both linguistic and dynamic visual ontologies are presented and their implementation for soccer video domain is shown and the structure of the proposed ontology itself can be used to perform higher-level annotation of the clips.
Abstract: Effective usage of multimedia digital libraries has to deal with the problem of building efficient content annotation and retrieval tools. In this paper multimedia ontologies, that include both linguistic and dynamic visual ontologies, are presented and their implementation for soccer video domain is shown. The structure of the proposed ontology itself, together with reasoning, can be used to perform higher-level annotation of the clips, to generate complex queries that comprise actions and their temporal evolutions and relations and to create extended text commentaries of video sequences.

63 citations

Journal ArticleDOI
TL;DR: This article first analyzes each module of a typical ILE, showing how it can benefit from the Social Semantic Web paradigm and then investigates how this new paradigm can be leveraged for increasing interactivity level of ILEs.
Abstract: Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments (ILEs) in particular, is related to the ability of leveraging these new paradigms for creating, maintaining and sharing the knowledge that these systems embed. This will enable ILEs to benefit from shared information from disparate systems, which is related to learning content and student activities, so that the overall complexity of system development and maintenance would be reduced while at the same time improving the capability of personalization, context-awareness, and interaction. In this article, we investigate how the Social Semantic Web can be leveraged for enabling and easing this process. We first analyze each module of a typical ILE, showing how it can ...

49 citations

Proceedings ArticleDOI
01 Jul 2008
TL;DR: This paper proposes a collaborative semantic-rich learning environment in which folksonomies created from studentspsila collaborative tags contribute to ontology maintenance, and teacher-directed feedback.
Abstract: The social semantic Web has recently emerged as a paradigm in which ontologies (aimed at defining, structuring and sharing information) and collaborative software (used for creating and sharing knowledge) have been merged together. Ontologies provide an effective means of capturing and integrating knowledge for feedback provisioning, while using collaborative activities can support pedagogical theories, such as social constructivism. Both technologies have developed separately in the e-learning domain; representing respectively a teacher-centered and a learner-centered approach for learning environments. In this paper we bridge the gap between these two approaches by leveraging the social semantic Web paradigm, and propose a collaborative semantic-rich learning environment in which folksonomies created from studentspsila collaborative tags contribute to ontology maintenance, and teacher-directed feedback.

46 citations


Cited by
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Book
05 Jun 2007
TL;DR: The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content.
Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

2,579 citations

Journal ArticleDOI
TL;DR: Improvements and expansions to several branches of the Gene Ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community are described.
Abstract: The Gene Ontology (GO; http://wwwgeneontologyorg) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology

2,529 citations

Journal ArticleDOI
Seth Carbon1, Eric Douglass1, Nathan Dunn1, Benjamin M. Good1  +189 moreInstitutions (19)
TL;DR: GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models.
Abstract: The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.

2,138 citations

Journal ArticleDOI
27 Mar 2014-Nature
TL;DR: For example, the authors mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body.
Abstract: Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research

1,715 citations

Journal ArticleDOI
01 Nov 2017-Nature
TL;DR: A meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project is presented, creating both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity.
Abstract: Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity.

1,676 citations