Martin J. O'Connor

Mapping master: a flexible approach for mapping spreadsheets to OWL

We describe a mapping language for converting data contained in spreadsheets into the Web Ontology Language (OWL). The developed language, called M2, overcomes shortcomings with existing mapping techniques, including their restriction to well-formed spreadsheets reminiscent of a single relational database table and verbose syntax for expressing mapping rules when transforming spreadsheet contents into OWL. The M2 language provides expressive, yet concise mechanisms to create both individual and class axioms when generating OWL ontologies. We additionally present an implementation of the mapping approach, Mapping Master, which is available as a plug-in for the Protege ontology editor.

Using Semantic Web Technologies for Knowledge-Driven Querying of Biomedical Data

Software applications that work with biomedical data have significant knowledge-management requirements. Formal knowledge models and knowledge-based methods can be very useful in meeting these requirements. However, most biomedical data are stored in relational databases, a practice that will continue for the foreseeable future. Using these data in knowledge-driven applications requires approaches that can form a bridge between relational models and knowledge models. Accomplishing this task efficiently is a research challenge. To address this problem, we have developed an end-to-end knowledge-based system based on Semantic Web technologies. It permits formal design-time specification of the data requirements of a system and uses those requirements to drive knowledge-driven queries on operational relational data in a deployed system. We have implemented a dynamic OWL-to-relational mapping method and used SWRL, the Semantic Web Rule Language, as a high-level query language that uses these mappings. We have used these methods to support the development of a participant tracking application for clinical trials and in the development of a test bed for evaluating biosurveillance methods.

Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-detection Algorithms

OBJECTIVE Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative. DESIGN We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner. MEASUREMENT We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms. RESULTS Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results. CONCLUSION The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings.

Overcoming the ontology enrichment bottleneck with Quick Term Templates

When developing the Ontology of Biomedical Investigations (OBI), the process of adding classes with similar patterns of logical definition is time consuming, error prone, and requires an editor to have some expertise in OWL. Moreover, the process is poorly suited for a large number of domain experts who have limited experience with ontology development, and this can hinder contributions. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions, yet requires minimal manual intervention by the OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format that is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment as evidenced by encouraging results obtained when evaluated with an early version of the MappingMaster Protege plugin.

Querying the semantic web with SWRL

The SWRLTab is a development environment for working with SWRL rules in Protege-OWL. It supports the editing and execution of SWRL rules. It also provides mechanisms to allow interoperation with a variety of rule engines and the incorporation of user-defined libraries of methods that can be used in rules. Several built-in libraries are provided, include collections of mathematical, string, and temporal operators, in addition to operators than can be used to effectively turn SWRL into a query language. This language provides a simple but powerful means of extracting information from OWL ontologies. Used in association with a relational data importation tool that we have developed called DataMaster, this query language can be also used to express knowledge-level queries on data imported from relational databases.

Epoch: an ontological framework to support clinical trials management

The increasing complexity of clinical trials has generated an enormous requirement for knowledge and information specification at all stages of the trials, including planning, documentation, implementation, and analysis. We are building a knowledge-based framework (Epoch) to support the management of clinical trials. We are tailoring this approach to the Immune Tolerance Network (ITN), an international research consortium developing new therapeutics in immune-mediated disorders. In the broad spectrum of trial management activities, we currently target two areas that are vital to the successful implementation of a trial: (1) tracking study participants as they advance through the trials, and (2) tracking biological specimens as they are processed at the trial laboratories. The core of our software architecture is a suite of ontologies that conceptualizes relevant clinical trial domain. Our approach can provide ITN and other research organizations a stable and consistent knowledge source for clinical-trial software applications.

Knowledge-data integration for temporal reasoning in a clinical trial system

Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing and integrating temporal data and domain knowledge is difficult with the database technologies used in most clinical research systems. There is often a disconnect between the database representation of research data and corresponding domain knowledge of clinical research concepts. In this paper, we present a set of methodologies for undertaking ontology-based specification of temporal information, and discuss their application to the verification of protocol-specific temporal constraints among clinical trial activities. Our approach allows knowledge-level temporal constraints to be evaluated against operational trial data stored in relational databases. We show how the Semantic Web ontology and rule languages OWL and SWRL, respectively, can support tools for research data management that automatically integrate low-level representations of relational data with high-level domain concepts used in study design.

Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protege-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.

An Ontology-Driven Framework for Deploying JADE Agent Systems

Multi-agent systems have proven to be a powerful technology for building distributed applications. However, the process of designing, configuring and deploying agent-based applications is still primarily a manual one. There is a need for mechanisms and tools to help automate the many development steps required when building these applications. Using the Semantic Web ontology language OWL and the JADE platform we have developed a number of models and software tools that provide an end-to-end solution for designing and deploying agent-based systems. This solution supports the construction of detailed models of agent behavior and the automatic deployment of agents from those models. We illustrate its use in the construction of a multi-agent system that supports the configuration, deployment, and evaluation of analytic methods for detecting disease outbreaks.

Adaptive System for Collaborative Online Laboratories

Group activities are important aspects of the traditional laboratory experience. A framework using Semantic Web technologies supports collaborative strategies for online laboratories as well.