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.

/pdf/visual-genome-connecting-language-and-vision-using-15ih2ylk6r.pdf

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.

/pdf/yago-a-core-of-semantic-knowledge-13k3nmj6l2.pdf

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.

/pdf/knowledge-engineering-principles-and-methods-6do1hbj75r.pdf

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.

/pdf/the-nhgri-gwas-catalog-a-curated-resource-of-snp-trait-3zt2r8ota7.pdf

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

Ontologies and Semantic Web Services are the two core technologies of the Semantic Web. The Semantic Web hinges on the ability of computer programs to perform some task involving the autonomous resolution of semantic issues. This ability requires providing standard access for software to ontologies. Moreover, for the Semantic Web to gain widespread acceptance, it needs to reach a critical mass of applications that can interact. This last point requires providing standard access to functionalities for manipulating ontologies. Therefore, it is relevant to bring ontologies and Web Services together by providing access to ontologies through Semantic Web Services. We analyse different kinds of ontology-manipulation functionalities that could be implemented as ontology Web Services (OWS). We then propose an architecture allowing programs to insert calls to ontology Web Services into the more general framework of Web Services. We show that this architecture is a necessary complement to OWL-S for Semantic Web applications to perform dynamic discovery and invocation of Web Services, thus addressing a key requirement of the Semantic Web. We then demonstrate the scalability of our architecture as it allows the composition of (ontology) Web Services for performing complex tasks. 1 Web Service Access to Ontologies Ontologies and Semantic Web Services are arguably the two core technologies of the Semantic Web. Ontologies provide the backbone of the Semantic Web, defining the semantics of the data and Web resources. Web Services enable programs to call functions provided by a remote server. The structure of the parameters and of the result of the function are represented in an explicit way so that the service can be invoked by any client. Web Services, albeit not yet Semantic Web Services, have become important components of business applications. Currently however, most examples of Web Services (and in fact Semantic Web Services) operate on data that conforms to some schema or ontology. At the same time, ontologies themselves are first-class objects on the Semantic Web. Therefore, we believe that any infrastructure for Semantic Web Services will be incomplete without components to access and manipulate ontologies themselves through Web Services. Such access and manipulation include query of ontology

http://www.ai.sri.com/SWS2004/final-versions/SWS2004-Dameron-Final.pdf

Accessing and Manipulating Ontologies Using Web Services.

Scientists in the Earth and Environmental Sciences (EES) domain increasingly use ontologies to analyze and integrate their data. For example, the NASA’s SWEET ontologies (Semantic Web for Earth and Environmental Terminology) have become the de facto standard ontologies to represent the EES domain formally (Raskin 2010). Now we must develop principled ways both to evaluate existing ontologies and to ascertain their quality in a quantitative manner. Existing literature describes many potential quality metrics for ontologies. Among these metrics is the coverage metric, which approximates the relevancy of an ontology to a corpus (Yao et al. (PLoS Comput Biol 7(1):e1001055+, 2011)). This paper has three primary contributions to the EES domain: (1) we present an investigation of the applicability of existing coverage techniques for the EES domain; (2) we present a novel expansion of existing techniques that uses thesauri to generate equivalence and subclass axioms automatically; and (3) we present an experiment to establish an upper-bound coverage expectation for the SWEET ontologies against real-world EES corpora from DataONE (Michener et al. (Ecol Inform 11:5–15, 2012)), and a corpus designed from research articles to specifically match the topics covered by the SWEET ontologies. This initial evaluation suggests that the SWEET ontology can accurately represent real corpora within the EES domain.

SWEET ontology coverage for earth system sciences

PROMPT: Algorithm and Tool for Automated Ontology Merging and

To achieve better interoperability among intelligent applications, and to relieve knowledge engineers from the burden of developing ontologies from scratch, it is critical to reuse ontologies. However, there are two main reasons why the reuse of ontologies is rare: (1) current ontology repositories allow only simple keyword-based search facilities, and (2) even when a user finds an ontology, the information about the ontology quality and (re)usability is not available. In this paper, we present an Open Rating System based approach for ontology evaluation. The core Open Rating System model is extended with topic-specific trust to provide more accurate personalized ontology rankings. Our model is partially implemented in Knowledge Zone—a webbased ontology repository where users can submit their ontologies, annotate them with metadata, search for existing ontologies, find out their rankings based on user reviews, post their own reviews, and rate reviews.

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Topic-Specific Trust and Open Rating Systems: An Approach for Ontology Evaluation.

Ontology mapping is the key to data interoperability in the semantic web vision. Computing mappings is the first step to applications such as query rewriting, instance sharing, web-service integration, and ontology merging. This problem has received a lot of attention in recent years, but little is known about how users actually construct mappings. Several ontology-mapping tools have been developed, but which tools do users actually use? What processes are users following to discover, track, and compute mappings? How do teams coordinate when performing mappings? In this paper, we discuss the results from an online user survey where we gathered feedback from the community to help answer these important questions. We discuss the results from the survey and the implications they may have on the mapping research community.

/pdf/ontology-mapping-a-user-survey-47w5olwp5c.pdf

Natalya F. Noy

Papers

Accessing and Manipulating Ontologies Using Web Services.

SWEET ontology coverage for earth system sciences

PROMPT: Algorithm and Tool for Automated Ontology Merging and

Topic-Specific Trust and Open Rating Systems: An Approach for Ontology Evaluation.

Ontology Mapping - a User Survey