Showing papers on "Ontology (information science) published in 2017"

Expansion of the Gene Ontology knowledgebase and resources

Abstract: The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.

The Human Phenotype Ontology in 2017

Abstract: Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.

Abstract: In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

Surgical data science for next-generation interventions.

Abstract: Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.

CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System

Abstract: Great efforts have been dedicated to harvesting knowledge bases from online encyclopedias These knowledge bases play important roles in enabling machines to understand texts However, most current knowledge bases are in English and non-English knowledge bases, especially Chinese ones, are still very rare Many previous systems that extract knowledge from online encyclopedias, although are applicable for building a Chinese knowledge base, still suffer from two challenges The first is that it requires great human efforts to construct an ontology and build a supervised knowledge extraction model The second is that the update frequency of knowledge bases is very slow To solve these challenges, we propose a never-ending Chinese Knowledge extraction system, CN-DBpedia, which can automatically generate a knowledge base that is of ever-increasing in size and constantly updated Specially, we reduce the human costs by reusing the ontology of existing knowledge bases and building an end-to-end facts extraction model We further propose a smart active update strategy to keep the freshness of our knowledge base with little human costs The 164 million API calls of the published services justify the success of our system

Multiple ontologies of water: Politics, conflict and implications for governance:

Abstract: We ask what it would mean to take seriously the possibility of multiple water ontologies, and what the implications of this would be for water governance in theory and practice. We contribute to a ...

A Standardized Diagnostic Ontology for Fibrotic Interstitial Lung Disease. An International Working Group Perspective

Abstract: A Standardized Diagnostic Ontology for Fibrotic Interstitial Lung Disease An International Working Group Perspective Christopher J. Ryerson, Tamera J. Corte, Joyce S. Lee, Luca Richeldi, Simon L. F. Walsh, Jeffrey L. Myers, Jürgen Behr, Vincent Cottin, Sonye K. Danoff, Kevin R. Flaherty, David J. Lederer, David A. Lynch, Fernando J. Martinez, Ganesh Raghu, William D. Travis, Zarir Udwadia, Athol U. Wells, and Harold R. Collard

Behavior change interventions: the potential of ontologies for advancing science and practice

Abstract: A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.

Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence

Abstract: Threat intelligence is the provision of evidence-based knowledge about existing or potential threats Benefits of threat intelligence include improved efficiency and effectiveness in security operations in terms of detective and preventive capabilities Successful threat intelligence within the cyber domain demands a knowledge base of threat information and an expressive way to represent this knowledge This purpose is served by the use of taxonomies, sharing standards, and ontologiesThis paper introduces the Cyber Threat Intelligence (CTI) model, which enables cyber defenders to explore their threat intelligence capabilities and understand their position against the ever-changing cyber threat landscape In addition, we use our model to analyze and evaluate several existing taxonomies, sharing standards, and ontologies relevant to cyber threat intelligence Our results show that the cyber security community lacks an ontology covering the complete spectrum of threat intelligence To conclude, we argue the importance of developing a multi-layered cyber threat intelligence ontology based on the CTI model and the steps should be taken under consideration, which are the foundation of our future work

The Gene Ontology and the Meaning of Biological Function.

Abstract: The Gene Ontology (GO) provides a framework and set of concepts for describing the functions of gene products from all organisms. It is specifically designed for supporting the computational representation of biological systems. A GO annotation is an association between a specific gene product and a GO concept, together making a statement pertinent to the function of that gene. However, the meaning of the term "function" is not as straightforward as it might seem, and has been discussed at length in both philosophical and biological circles. Here, I first review these discussions. I then present an explicit formulation of the biological model that underlies the GO and annotations, and discuss how this model relates to the broader debates on the meaning of biological function.

IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

Abstract: Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.

Abstract: Motivation The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. Methods We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. Results The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. Availability and implementation A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . Contact gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr.

Semantic Reasoning for Context-Aware Internet of Things Applications

Abstract: Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

Geographical relational poverty studies

Abstract: Relationality is a persistent concern of socio-spatial theory, increasingly invoked in geographical scholarship. We bring geographical scholarship on relationality to bear on relational poverty stu...

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration.

Abstract: Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies.

Towards a thesaurus of plant characteristics: an ecological contribution

Abstract: Ecological research produces a tremendous amount of data, but the diversity in scales and topics covered and the ways in which studies are carried out result in large numbers of small, idiosyncratic data sets using heterogeneous terminologies. Such heterogeneity can be attributed, in part, to a lack of standards for acquiring, organizing and describing data. Here, we propose a terminological resource, a Thesaurus Of Plant characteristics (TOP), whose aim is to harmonize and formalize concepts for plant characteristics widely used in ecology. TOP concentrates on two types of plant characteristics: traits and environmental associations. It builds on previous initiatives for several aspects: (i) characteristics are designed following the entity-quality (EQ) model (a characteristic is modelled as the ‘Quality’ of an ‘Entity’ ) used in the context of Open Biological Ontologies; (ii) whenever possible, the Entities and Qualities are taken from existing terminology standards, mainly the Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) ontologies; and (iii) whenever a characteristic already has a definition, if appropriate, it is reused and referenced. The development of TOP, which complies with semantic web principles, was carried out through the involvement of experts from both the ecology and the semantics research communities. Regular updates of TOP are planned, based on community feedback and involvement. TOP provides names, definitions, units, synonyms and related terms for about 850 plant characteristics. TOP is available online (www.top-thesaurus.org), and can be browsed using an alphabetical list of characteristics, a hierarchical tree of characteristics, a faceted and a free-text search, and through an Application Programming Interface. Synthesis. Harmonizing definitions of concepts, as proposed by TOP, forms the basis for better integration of data across heterogeneous data sets and terminologies, thereby increasing the potential for data reuse. It also allows enhanced scientific synthesis. TOP therefore has the potential to improve research and communication not only within the field of ecology, but also in related fields with interest in plant functioning and distribution.

An Ontology-based Context-aware System for Smart Homes: E-care@home.

Abstract: Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

Distance learning techniques for ontology similarity measuring and ontology mapping

Abstract: Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

Gene Ontology: Pitfalls, Biases, and Remedies.

Abstract: The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling☆

Abstract: Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protege web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.

Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies

Abstract: Under the background of cyber-physical systems and Industry 4.0, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data. Accordingly, it still faces various challenges. In this paper, we summarize and analyze the current research status in both domestic and aboard, including industrial big data collection, modeling of the intelligent product lines based on ontology, the predictive diagnosis based on industrial big data, group learning of product line equipment and the product line reconfiguration of intelligent manufacturing. Based on the research status and the problems, we propose the research strategies, including acquisition schemes of industrial big data under the environment of intelligent, ontology modeling and deduction method based intelligent product lines, predictive diagnostic methods on production lines based on deep neural network, deep learning among devices based on cloud supplements and 3-D self-organized reconfiguration mechanism based on the supplements of cloud. In our view, this paper will accelerate the implementation of smart factory.