Knowledge Base Version Reintegration

Given two versions of a knowledge base (KB), independently modified, we investigated the problem of incorporating changes made to one KB version into the other. We have implemented a system that will perform such a reintegration, autonomously, using predetermined user preferences. This effort has lead to a greater insight into the version reintegration problem and has highlighted those areas where user intervention would be the most beneficial in a semi-autonomous system

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

Querying Non-Materialized Ontology Views

One approach to simplifying ontologies, for inclusion in a more tractable semantic web, is through the use of non-materialized view queries. View queries define how a simplified “view” or “application” ontology is derived from larger more complex ontologies. In this work we look at a language for specifying view queries over OWL/RDFS sources, and we illustrate some initial ideas for how to execute user queries over our view ontology, without materializing it first

Value Sets via Ontology Views

We present a method for defining value sets as queries over ontologies (ontology views), and a mechanism for evaluating such queries. In particular we demonstrate an approach utilizing reusable template queries and parameterized URLs. We illustrate this method using an example from the Ontology of Clinical Research (OCRe)

Ontology View Query Management

Like views in relational databases, ontology views are expressed as queries, but over source ontologies rather than tables. To enhance the reusability of such views, we are constructing a view Query Manager application. The Query Manager allows queries to be edited, executed, and stored for reuse. View queries are discoverable by searching the Query Manager's metadata catalog. The Query Manager also supports the storage of materialized view results upon which further queries may be issued

An Intuitive Graphical Query Interface for Protégé Knowledge Bases

Emily is a graphical query engine for Protégé knowledge bases that was developed by the Structural Informatics Group (SIG) at the University of Washington. Currently this application is adapted for a specific knowledge model, the Foundational Model of Anatomy (FMA) [1], but it could readily be generalized for use with other Protégé knowledge bases. In developing the Emily query interface, our intent was to provide a tool that was simple and intuitive to use, like the Queries tab provided with Protégé, but with improved information retrieval capabilities. Although some more advanced query mechanisms exist, currently they are too complicated for non-expert end users. The Algernon tab [2], for example, provides extensive Protégé query capabilities but requires users to learn a query scripting language. We sought to develop a query interface that was intuitive enough for end users to operate, with only minor instruction, yet was powerful enough to gather interesting information from a knowledge base that was not easily attained by browsing alone

Distributed Queries for Quality Control Checks in Clinical Trials

Operational Quality Control (QC) checks are standard practice in clinical trials and ensure ongoing compliance with the study protocol, standard operating procedures (SOPs) and Good Clinical Practice (GCP). We present a method for defining QC checks as distributed queries over case report forms (CRF) and clinical imaging data- sources. Our distributed query system can integrate time-sensitive information in order to populate QC checks that can facilitate discrepancy resolution workflow in clinical trials

Integrating and Ranking Uncertain Scientific Data

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

Challenges in Reconciling Different Views of Neuroanatomy in a Reference Ontology of Anatomy

A fundamental requirement for integrating neuroscience data is a well-structured ontology that can incorporate, accommodate and reconcile different neuroanatomical views. Here we describe the challenges in creating such ontology, and, because of its principled design, illustrate the potential of the Foundational Model of Anatomy to be that ontology