By extending Cyc’s ontology and KB approximately 2%, Cycorp and Cleveland Clinic Foundation (CCF) have built a system to answer clinical researchers’ ad hoc queries. The query may be long and complex, hence only partially understood at first, parsed into a set of CycL (higher-order logic) fragments with open variables. But, surprisingly often, after applying various constraints (medical domain knowledge, common sense, discourse pragmatics, syntax), there is only one single way to fit those fragments together, one semantically meaningful formal query P. The system, SRA (for Semantic Research Assistant), dispatches a series of database calls and then combines, logically and arithmetically, their results into answers to P. Seeing the first few answers stream back, the user may realize that they need to abort, modify, and re-ask their query. Even before they push ASK, just knowing approximately how many answers would be returned can spark such editing. Besides real-time ad hoc query-answering, queries can be bundled and persist over time. One bundle of 275 queries is rerun quarterly by CCF to produce the procedures and outcomes data it needs to report to STS (Society of Thoracic Surgeons, an external hospital accreditation and ranking body); another bundle covers ACC (American College of Cardiology) reporting. Until full articulation/answering of precise, analytical queries becomes as straight-forward and ubiquitous as text search, even partial understanding of a query empowers semantic search over semi-structured data (ontology-tagged text), avoiding many of the false positives and false negatives that standard text searching suffers from.

Semantic Web technologies offer the potential to revolutionize management of health care data by increasing interoperability and reusability while reducing the need for redundant data collection and storage. From 1998 through 2010, Cleveland Clinic sponsored a project designed to explore and develop this potential. The product of this effort, SemanticDB, is a suite of software tools and knowledge resources built to facilitate the collection, storage and use of the diverse data needed to conduct clinical research and health care quality reporting. SemanticDB consists of three main components: 1) a content repository driven by a meta-model that facilitates collection and integration of data in an XML format and automatically converts the data to RDF; 2) an inference-mediated, natural language query interface designed to identify patients who meet complex inclusion and exclusion criteria; and 3) a data production pipeline that uses inference to generate customized views of the repository content for statistical analysis and reporting. Since

Software development involves stitching existing components together. These data/service components are usually not well understood, as they are made by others and often obtained from somewhere on the Internet. This makes software development a daunting challenge, requiring programmers to manually discover the resources they need, understand their capabilities, adapt these resources to their needs, and update the system as external components change.Software researchers have long realized the problem why automation seems impossible: the lack of semantic "understanding" on the part of the machine about those components. A multitude of solutions have been proposed under the umbrella term Semantic Web (SW), in which semantic markup of the components with concepts from semantic ontologies and the ability to invoke queries over those concepts enables a form of automated discovery and mediation among software services.On another front, programming languages rarely provide mechanisms for anchoring objects/data to real-world concepts. Inspired by the aspirations of SW, in this paper we reformulate its visions from the perspective of a programming model, i.e., that components themselves should be able to interact using semantic ontologies, rather than having a separate markup language and composition platform. In the vision, a rich specification language and common sense knowledge base over real-world concepts serves as a lingua franca to describe software components. Components can query the system to automatically (1) discover other components that provide needed functionality/data (2) discover the appropriate API within that component in order to obtain what is intended, and even (3) implicitly interpret the provided data in the desired form independent of the form originally presented by the provider component.By demonstrating a successful case of realization of this vision on a microexample, we hope to show how a programming languages (PL) approach to SW can be superior to existing engineered solutions, since the generality and expressiveness in the language can be harnessed, and encourage PL researchers to jump on the SW bandwagon.

LONG-TERM GOALSThis consortium project is attempting to use all existing ocean observations, including satellite data, for the purpose of understanding and ultimately predicting, the ocean on climate time-scales. To this end it is advancing ocean "state estimation" as a practical, quasi-operational tool, for studying the ocean circulation and its influence on societal problems such as climate change, sea level rise, and biological impacts. Observing the ocean is difficult owing to its turbulent nature and very large range of energetic spatial scales. This project is establishing the means by which a quantitative description of the global ocean is and will be routinely and continuously available. The methodology employs stateof-the-art general circulation models, statistical estimation techniques, and the complete range of available oceanic observations. The effort includes further demonstration of the practical utility of ocean observing systems through their use in important scientific goals.