Showing papers on "Ontology (information science) published in 2013"

Activities at the Universal Protein Resource (UniProt)

Abstract: The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.

Ontology Matching: State of the Art and Future Challenges

Abstract: After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

Large-scale visual sentiment ontology and detectors using adjective noun pairs

Abstract: We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.

System and method for fuzzy concept mapping, voting ontology crowd sourcing, and technology prediction

Abstract: The invention provides a system and method for providing ttx-based categorization services and a categorized commonplace of shared information. Currency of the contents is improved by a process called conjuring/concretizing wherein users' thoughts are rapidly infused into the Map. As a new idea is sought, a goal is created for a search. After the goal idea is found, a ttx is concretized and categorized. The needs met by such a Map are prior art searching, competitive environmental scanning, competitive analysis study repository management and reuse, innovation gap analysis indication, novelty checking, technology value prediction, investment area indication and planning, and product technology comparison and feature planning.

PROV-O: The PROV Ontology

Abstract: The PROV Ontology (PROV-O) expresses the PROV Data Model using the OWL2 Web Ontology Language. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains.

Ontology-based sentiment analysis of twitter posts

Abstract: The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.

Scaling Semantic Parsers with On-the-Fly Ontology Matching

Abstract: We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to represent in a fixed target ontology. For example, even simple phrases such as ‘daughter’ and ‘number of people living in’ cannot be directly represented in Freebase, whose ontology instead encodes facts about gender, parenthood, and population. In this paper, we introduce a new semantic parsing approach that learns to resolve such ontological mismatches. The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Experiments demonstrate state-of-the-art performance on two benchmark semantic parsing datasets, including a nine point accuracy improvement on a recent Freebase QA corpus.

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Abstract: Background: Statistical shape models are widely used in biomedical research. They are routinely implemented for automatic image segmentation or object identification in medical images. In these fields, however, the acquisition of the large training datasets, required to develop these models, is usually a time-consuming process. Even after this effort, the collections of datasets are often lost or mishandled resulting in replication of work. Objective: To solve these problems, the Virtual Skeleton Database (VSD) is proposed as a centralized storage system where the data necessary to build statistical shape models can be stored and shared. Methods: The VSD provides an online repository system tailored to the needs of the medical research community. The processing of the most common image file types, a statistical shape model framework, and an ontology-based search provide the generic tools to store, exchange, and retrieve digital medical datasets. The hosted data are accessible to the community, and collaborative research catalyzes their productivity. Results: To illustrate the need for an online repository for medical research, three exemplary projects of the VSD are presented: (1) an international collaboration to achieve improvement in cochlear surgery and implant optimization, (2) a population-based analysis of femoral fracture risk between genders, and (3) an online application developed for the evaluation and comparison of the segmentation of brain tumors. Conclusions: The VSD is a novel system for scientific collaboration for the medical image community with a data-centric concept and semantically driven search option for anatomical structures. The repository has been proven to be a useful tool for collaborative model building, as a resource for biomechanical population studies, or to enhance segmentation algorithms. [J Med Internet Res 2013;15(11):e245]

The environment ontology: contextualising biological and biomedical entities

Abstract: As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; http://www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from http://purl.obolibrary.org/obo/envo.owl - an OBO format version is also available by switching the file suffix to “obo”.

The AgreementMakerLight Ontology Matching System

Abstract: AgreementMaker is one of the leading ontology matching systems, thanks to its combination of a flexible and extensible framework with a comprehensive user interface. In many domains, such as the biomedical, ontologies are becoming increasingly large thus presenting new challenges. We have developed a new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework. We evaluated the efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large Biomedical Ontologies, obtaining excellent run time results. In addition, for the Anatomy track, AgreementMakerLight is now the best system as measured in terms of F-measure. Also in terms of F-measure, AgreementMakerLight is competitive with the best OAEI performers in two of the three tasks of the Large Biomedical Ontologies track that match whole ontologies.

EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats

Abstract: Motivation: Advancing the search, publication and integration of bioinformatics tools and resources demands consistent machine-understandable descriptions. A comprehensive ontology allowing such descriptions is therefore required. Results: EDAM is an ontology of bioinformatics operations (tool or workflow functions), types of data and identifiers, application domains and data formats. EDAM supports semantic annotation of diverse entities such as Web services, databases, programmatic libraries, standalone tools, interactive applications, data schemas, datasets and publications within bioinformatics. EDAM applies to organizing and finding suitable tools and data and to automating their integration into complex applications or workflows. It includes over 2200 defined concepts and has successfully been used for annotations and implementations. Availability: The latest stable version of EDAM is available in OWL format from http://edamontology.org/EDAM.owl and in OBO format from http://edamontology.org/EDAM.obo. It can be viewed online at the NCBO BioPortal and the EBI Ontology Lookup Service. For documentation and license please refer to http://edamontology.org. This article describes version 1.2 available at http://edamontology.org/ EDAM_1.2.owl.

PhenoTips: patient phenotyping software for clinical and research use.

Abstract: We have developed PhenoTips: open source software for collecting and analyzing phenotypic information for patients with genetic disorders. Our software combines an easy-to-use interface, compatible with any device that runs a Web browser, with a standardized database back end. The PhenoTips' user interface closely mirrors clinician workflows so as to facilitate the recording of observations made during the patient encounter. Collected data include demographics, medical history, family history, physical and laboratory measurements, physical findings, and additional notes. Phenotypic information is represented using the Human Phenotype Ontology; however, the complexity of the ontology is hidden behind a user interface, which combines simple selection of common phenotypes with error-tolerant, predictive search of the entire ontology. PhenoTips supports accurate diagnosis by analyzing the entered data, then suggesting additional clinical investigations and providing Online Mendelian Inheritance in Man (OMIM) links to likely disorders. By collecting, classifying, and analyzing phenotypic information during the patient encounter, PhenoTips allows for streamlining of clinic workflow, efficient data entry, improved diagnosis, standardization of collected patient phenotypes, and sharing of anonymized patient phenotype data for the study of rare disorders. Our source code and a demo version of PhenoTips are available at http://phenotips.org.

Integrating NLP Using Linked Data

Abstract: We are currently observing a plethora of Natural Language Processing tools and services being made available. Each of the tools and services has its particular strengths and weaknesses, but exploiting the strengths and synergistically combining different tools is currently an extremely cumbersome and time consuming task. Also, once a particular set of tools is integrated, this integration is not reusable by others. We argue that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications. In this paper, we present the NLP Interchange Format (NIF). NIF is based on a Linked Data enabled URI scheme for identifying elements in (hyper-)texts and an ontology for describing common NLP terms and concepts. In contrast to more centralized solutions such as UIMA and GATE, NIF enables the creation of heterogeneous, distributed and loosely coupled NLP applications, which use the Web as an integration platform. We present several use cases of the second version of the NIF specification (NIF 2.0) and the result of a developer study.

A video game description language for model-based or interactive learning

Abstract: We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.

A gene ontology inferred from molecular networks

Abstract: Ontologies have proven very useful for capturing knowledge as a hierarchy of terms and their interrelationships. In biology a major challenge has been to construct ontologies of gene function given incomplete biological knowledge and inconsistencies in how this knowledge is manually curated. Here we show that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to infer an ontology whose coverage and power are equivalent to those of the manually curated Gene Ontology (GO). The network-extracted ontology (NeXO) contains 4,123 biological terms and 5,766 term-term relations, capturing 58% of known cellular components. We also explore robust NeXO terms and term relations that were initially not cataloged in GO, a number of which have now been added based on our analysis. Using quantitative genetic interaction profiling and chemogenomics, we find further support for many of the uncharacterized terms identified by NeXO, including multisubunit structures related to protein trafficking or mitochondrial function. This work enables a shift from using ontologies to evaluate data to using data to construct and evaluate ontologies.

SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content

Abstract: A picture is worth one thousand words, but what words should be used to describe the sentiment and emotions conveyed in the increasingly popular social multimedia? We demonstrate a novel system which combines sound structures from psychology and the folksonomy extracted from social multimedia to develop a large visual sentiment ontology consisting of 1,200 concepts and associated classifiers called SentiBank. Each concept, defined as an Adjective Noun Pair (ANP), is made of an adjective strongly indicating emotions and a noun corresponding to objects or scenes that have a reasonable prospect of automatic detection. We believe such large-scale visual classifiers offer a powerful mid-level semantic representation enabling high-level sentiment analysis of social multimedia. We demonstrate novel applications made possible by SentiBank including live sentiment prediction of social media and visualization of visual content in a rich intuitive semantic space.

The Appearance of Data

Abstract: This paper explores conditions that allow data to appear, to come into being, in both conventional and more radical approaches in empirical social science research. Conventional qualitative inquiry that uses a positivist ontology—even when it claims to be interpretive—treats qualitative data, words, as brute, existing independent of an interpretive frame, waiting to be “collected” by a human. However, a Deleuzo-Guattarian ontology that does not assume the subject/object binary might not think the concept data at all. The author resists recuperating data in the collapse of the old empiricism and is content to pause in the curious possibilities of a normative ontology that imagines a superior, affirmative, and experimental empiricism in which all concepts, including data, must be re-thought.

Ontology of units of measure and related concepts

Abstract: This paper describes the Ontology of units of Measure and related concepts OM, an OWL ontology of the domain of quantities and units of measure. OM supports making quantitative research data more explicit, so that the data can be integrated, verified and reproduced. The various options for modeling the domain are discussed. For example, physical quantities can be modeled either as classes, instances or properties. The design choices made are based on use cases from our own projects and general experience in the field. The use cases have been implemented as tools and web services. OM is compared with QUDT, another active effort for an OWL model in this domain. We note possibilities for integration of these efforts. We also discuss the role OWL plays in our approach.

The Plant Ontology as a Tool for Comparative Plant Anatomy and Genomic Analyses

Abstract: The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary (‘ontology’) of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.

Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services

Abstract: There is a critical need to develop a mobile-based emergency response system (MERS) to help reduce risks in emergency situations. Existing systems only provide short message service (SMS) notifications, and the decision support is weak, especially in man-made disaster situations. This paper presents a MERS ontology-supported case-based reasoning (OS-CBR) method, with implementation, to support emergency decision makers to effectively respond to emergencies. The advantages of the OS-CBR approach is that it builds a case retrieving process, which provides a more convenient system for decision support based on knowledge from, and solutions provided for past disaster events. The OS-CBR approach includes a set of algorithms that have been successfully implemented in four components: data acquisition; ontology; knowledge base; and reasoning; as a sub-system of the MERS framework. A set of experiments and case studies validated the OS-CBR approach and application, and demonstrate its efficiency.

Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings

Abstract: Conducting a safe and successful major event highly depends on the effective provision of medical emergency services that are often offered by different public and private agencies. Poor communication and coordination between these agencies and teams can result in delays in decision-making and duplication of efforts. Another related issue is that emergency decisions are usually made based on individual experience and domain knowledge of relevant managerial personnel. For sustainable knowledge management and more intelligent decision support it is beneficial to collect, consolidate, store and share these experiences in a form of a knowledge base or domain ontology. State-of-the-art surveys identify this gap that there is no common ontology describing the domain knowledge for planning and managing medical services in mass gatherings. Part of the reason is that the process of construction of such an ontology is not a trivial task. In this paper, we describe the process of developing and evaluating a Domain Ontology for Mass Gatherings (DO4MG) with a focus on medical emergency management. As part of the evaluation, we illustrate the application of DO4MG for implementing a case-based reasoning decision support for emergency medical management in mass gatherings. Such an implementation demonstrates the potential of using ontology for resolving terminology inconsistencies and their usefulness for supporting communication between medical emergency personnel in mass gatherings. We also illustrate how this ontology can be applied to different stages of medical emergency management as part of a system architecture. The lessons learnt from building DO4MG for this domain could be beneficial in general to the theory and practice of intelligent decision support and knowledge management in complex problem domains. Highlights? We describe how to improve medical emergency decision-making applying ontology. ? Systematic method for development and evaluation of such an ontology is described. ? Intelligent decision support system architecture incorporating ontology is proposed.

Ontology-based data access: a study through disjunctive datalog, CSP, and MMSNP

Abstract: Ontology-based data access is concerned with querying incomplete data sources in the presence of domain-specific knowledge provided by an ontology. A central notion in this setting is that of an ontology-mediated query, which is a database query coupled with an ontology. In this paper, we study several classes of ontology-mediated queries, where the database queries are given as some form of conjunctive query and the ontologies are formulated in description logics or other relevant fragments of first-order logic, such as the guarded fragment and the unary-negation fragment. The contributions of the paper are three-fold. First, we characterize the expressive power of ontology-mediated queries in terms of fragments of disjunctive datalog. Second, we establish intimate connections between ontology-mediated queries and constraint satisfaction problems (CSPs) and their logical generalization, MMSNP formulas. Third, we exploit these connections to obtain new results regarding (i) first-order rewritability and datalog-rewritability of ontology-mediated queries, (ii) P/NP dichotomies for ontology-mediated queries, and (iii) the query containment problem for ontology-mediated queries.

Bio2RDF release 2: Improved coverage, interoperability and provenance of life science linked data

Abstract: Bio2RDF currently provides the largest network of Linked Data for the Life Sciences. Here, we describe a significant update to increase the overall quality of RDFized datasets generated from open scripts powered by an API to generate registry-validated IRIs, dataset provenance and metrics, SPARQL endpoints, downloadable RDF and database files. We demonstrate federated SPARQL queries within and across the Bio2RDF network, including semantic integration using the Semanticscience Integrated Ontology (SIO). This work forms a strong foundation for increased coverage and continuous integration of data in the life sciences.