The REDCap consortium: Building an international community of software platform partners.

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of $$35$$35 objects, $$26$$26 attributes, and $$21$$21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.

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Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

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Yago: a core of semantic knowledge

This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

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Knowledge engineering: principles and methods

The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS assaying at least 100000 singlenucleotide polymorphisms (SNPs) and all SNP-trait associations with P <110 5 . The Catalog includes 1751 curated publications of 11912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs’ chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.

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The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

In numerous distributed environments, including today's World Wide Web, enterprise data management systems, large science projects, and the emerging semantic web, applications will inevitably use the information described by multiple ontologies and schemas. We organized the Workshop on Semantic Integration at the Second International Semantic Web Conference to bring together different communities working on the issues of enabling integration among different resources. The workshop generated a lot of interest and attracted more than 70 participants.

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Semantic integration workshop at the second international semantic web conference (ISWC-2003)

We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links.

We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments.

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Context-Specific Ontology Integration: A Bayesian Approach

Biomedical ontologies such as the 11th revision of the International Classification of Diseases and others are increasingly produced with the help of collaborative ontology engineering platforms that facilitate cooperation and coordination among a large number of users and contributors. While collaborative approaches to engineering biomedical ontologies can be expected to yield a number of advantages, such as increased participation and coverage, they come with a number of novel challenges and risks. For example, they might suffer from low participation, lack of coordination, lack of control or other related problems that are neither well understood nor addressed by the current state of research. In this paper, we aim to tackle some of these problems by exploring techniques for recommending concepts to experts on collaborative ontology engineering platforms. In detail, this paper will (i) discuss different recommendation techniques from the literature (ii) map and apply these categories to the domain of collaboratively engineered biomedical ontologies and (iii) present prototypical implementations of selected recommendation techniques as

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Recommending Concepts to Experts: An Exploration of Recommender Techniques for Collaborative Ontology Engineering Platforms in the Biomedical Domain

Ontologies underpin methods throughout biomedicine and biomedical informatics. However, as ontologies increase in size and complexity, so does the likelihood that they contain errors. Effective methods that identify errors are typically manual and expert-driven; however, automated methods are essential for the size of modern biomedical ontologies. The effect of ontology errors on their application is unclear, creating a challenge in differentiating salient, relevant errors with those that have no discernable effect. As a first step in understanding the challenge of identifying salient, common errors at a large scale, we asked 5 experts to verify a random subset of complex relations in the SNOMED CT CORE Problem List Subset. The experts found 39 errors that followed several common patterns. Initially, the experts disagreed about errors almost entirely, indicating that ontology verification is very difficult and requires many eyes on the task. It is clear that additional empirically-based, application-focused ontology verification method development is necessary. Toward that end, we developed a taxonomy that can serve as a checklist to consult during ontology quality assurance.

An empirically derived taxonomy of errors in SNOMED CT

Automated surveillance for disease detection requires the integration of many kinds of data. The clinical information systems in hospitals, clinics, and emergency rooms that record patient findings and conditions are a fruitful source of information that may suggest the presence of incipient epidemics in a timely manner. Analysis of data from electronic patient record systems requires methods that can integrate both numeric and nonnumeric information, and that can use the clinical context to aid in data interpretation. Techniques from artificial intelligence that can take advantage of clinical knowledge that has been encoded in machine-processable form are particularly well suited for disease surveillance. We describe the knowledge-based temporal abstraction (KBTA) method, which we believe will be particularly useful in performing automated surveillance of clinical data to detect occult acts of bioterrorism.

Natalya F. Noy

Papers

Semantic integration workshop at the second international semantic web conference (ISWC-2003)

Context-Specific Ontology Integration: A Bayesian Approach

Recommending Concepts to Experts: An Exploration of Recommender Techniques for Collaborative Ontology Engineering Platforms in the Biomedical Domain

An empirically derived taxonomy of errors in SNOMED CT

A knowledge-based approach to temporal abstraction of clinical data for disease surveillance