Martin J. O’Connor

A Method for Representing and Querying Temporal Information in OWL

Ontologies are becoming a core technology for supporting the sharing, integration, and management of information sources in Semantic Web applications. As critical as ontologies have become, ontology languages such as OWL typically provide minimal support for modeling the complex temporal information often contained in these sources. As a result, ontologies often cannot fully express the temporal knowledge needed by many applications, forcing users and developers to develop ad hoc solutions. In this paper, we present a methodology and a set of tools for representing and querying temporal information in OWL ontologies. The approach uses a lightweight temporal model to encode the temporal dimension of data. It also uses the OWL-based Semantic Web Rule Language (SWRL) and the SWRL-based OWL query language SQWRL to reason with and query the temporal information represented using our model.

The center for expanded data annotation and retrieval

The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments.

Clustering rule bases using ontology-based similarity measures

Abstract Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology-based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web.

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain

BackgroundA variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text.ResultsUsing an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test.ConclusionsOur work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation

BackgroundOntologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms.MethodsWe developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data.ResultsOur evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios.ConclusionsOntology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender, and via a Web service API).

Towards semantic interoperability in a clinical trials management system

Clinical trials are studies in human patients to evaluate the safety and effectiveness of new therapies. Managing a clinical trial from its inception to completion typically involves multiple disparate applications facilitating activities such as trial design specification, clinical sites management, participants tracking, and trial data analysis. There remains however a strong impetus to integrate these diverse applications – each supporting different but related functions of clinical trial management – at syntactic and semantic levels so as to improve clarity, consistency and correctness in specifying clinical trials, and in acquiring and analyzing clinical data. The situation becomes especially critical with the need to manage multiple clinical trials at various sites, and to facilitate meta-analyses on trials. This paper introduces a knowledge-based framework that we are building to support a suite of clinical trial management applications. Our initiative uses semantic technologies to provide a consistent basis for the applications to interoperate. We are adapting this approach to the Immune Tolerance Network (ITN), an international research consortium developing new therapeutics in immune-mediated disorders.

Representing and Reasoning with Temporal Constraints in Clinical Trials Using Semantic Technologies

Clinical trial protocols include schedule of clinical trial activities such as clinical tests, procedures, and medications. The schedule specifies temporal constraints on the sequence of these activities, on their start times and duration, and on their potential repetitions. There is an enormous requirement to conform to the constraints found in the protocols during the conduct of the clinical trials. In this paper, we present our approach to formally represent temporal constraints found in clinical trials, and to facilitate reasoning with the constraints. We have identified a representative set of temporal constraints found in clinical trials in the immune tolerance area, and have developed a temporal constraint ontology that allows us to formulate the temporal constraints to the extent required to support clinical trials management. We use the ontology to specify temporal annotation on clinical activities in an encoded clinical trial protocol. We have developed a temporal model to encapsulate time-stamped data, and to facilitate interval-based temporal operations on the data. Using semantic web technologies, we are building a knowledge-based framework that integrates the temporal constraint ontology with the temporal model to support queries on clinical trial data. Using our approach, we can formally specify temporal constraints, and reason with the temporal knowledge to support management of clinical trials.

A knowledge-based approach to defining syndromes

S i129 robust and extensible structure for rapidly characterizing, describing, and communicating nontraditional data. A Knowledge-Based Approach to Defining Syndromes Justin V. Graham, David L. Buckeridge, Zach Pincus, Michael K. Choy, Martin J, O’Connor, and Mark A. Musen Stanford Medical Informatics, Stanford University School of Medicine Syndromic surveillance can only produce meaningful results if there is a common understanding of what observations constitute a syndrome and consequently how a syndrome relates to diseases that may cause those observations. However, the constituent elements of syndromes, such as “flulike illness,” are poorly characterized and rarely explicitly defined by surveillance system developers. We describe here a preliminary ontology for the creation of bioterrorism syndrome knowledge bases that will facilitate sharing and comparison of knowledge independent of a particular system or research group. In addition, we have created an inference heuristic problem-solving method that can relate indirect measurements of disease to diseases of interest. Our ontology enables precise enunciation of forms of evidence required to diagnose a syndrome. The ontology contains six major categories: syndrome, syndrome modifier, system affected, sign/symptom, direct supporting evidence, and indirect supporting evidence. We have instantiated the ontology for the syndrome “bioweapon respiratory illness.” The inference heuristic can use the elements of this ontology to combine direct measurements into meaningful abstractions. Each sign and symptom has a defined, explicit relationship to supporting direct and indirect evidence, like measured temperature or a patient’s chief complaint. Similarly, the presence of a syndrome can only be inferred if illness within requisite body systems can be substantiated by the presence of symptoms. We propose that all developers of syndromic surveillance systems explicitly define their syndrome concepts using a standard ontology. Syndrome definitions can be stored as instantiated knowledge bases in a common central repository, permitting knowledge sharing and reuse. A Knowledge-Based Method for Surveillance David L. Buckeridge, Martin O’Connor, Justin Graham, Michael K. Choy, Zachary Pincus, and Mark Musen Stanford Medical Informatics, Stanford University School of Medicine Surveillance of prediagnostic “nontraditional” data sources (e.g., school absenteeism, pharmaceutical sales, emergency medical services calls) is expected to enhance the timeliness of epidemic detection. However, prediagnostic data are not as specific as diagnostic data, so multiple sources must be followed to reduce false-positive detections. Combined analysis of multiple nontraditional data sources requires knowledge about the relationships between data sources, but knowledge of these relationships is often qualitative and uncertain. Statistical methods perform well for focused analyses of quantitative data according to well-defined models. However, statistical models do not readily incorporate qualitative data and can become unwieldy as the number of parameters grows. A knowledge-based approach requires explicit representation of surveillance knowledge and tasks and enables knowledge to be applied to problem solving in a structured manner. Our research approach is to model the tasks involved in public health surveillance and the knowledge required to accomplish these tasks. Based on these models, we identify or develop problem-solving methods (PSMs) that accomplish surveillance tasks. This modular development approach enables controlled evaluation of different PSMs and knowledge representations in terms of epidemic detection and impact on decision making around interventions. Prototype methods have been implemented

The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories

The adaptation of high-throughput sequencing to the B cell receptor and T cell receptor has made it possible to characterize the adaptive immune receptor repertoire (AIRR) at unprecedented depth. These AIRR sequencing (AIRR-seq) studies offer tremendous potential to increase the understanding of adaptive immune responses in vaccinology, infectious disease, autoimmunity, and cancer. The increasingly wide application of AIRR-seq is leading to a critical mass of studies being deposited in the public domain, offering the possibility of novel scientific insights through secondary analyses and meta-analyses. However, effective sharing of these large-scale data remains a challenge. The AIRR community has proposed minimal information about adaptive immune receptor repertoire (MiAIRR), a standard for reporting AIRR-seq studies. The MiAIRR standard has been operationalized using the National Center for Biotechnology Information (NCBI) repositories. Submissions of AIRR-seq data to the NCBI repositories typically use a combination of web-based and flat-file templates and include only a minimal amount of terminology validation. As a result, AIRR-seq studies at the NCBI are often described using inconsistent terminologies, limiting scientists’ ability to access, find, interoperate, and reuse the data sets. In order to improve metadata quality and ease submission of AIRR-seq studies to the NCBI, we have leveraged the software framework developed by the Center for Expanded Data Annotation and Retrieval (CEDAR), which develops technologies involving the use of data standards and ontologies to improve metadata quality. The resulting CEDAR-AIRR (CAIRR) pipeline enables data submitters to: (i) create web-based templates whose entries are controlled by ontology terms, (ii) generate and validate metadata, and (iii) submit the ontology-linked metadata and sequence files (FASTQ) to the NCBI BioProject, BioSample, and Sequence Read Archive databases. Overall, CAIRR provides a web-based metadata submission interface that supports compliance with the MiAIRR standard. This pipeline is available at http://cairr.miairr.org, and will facilitate the NCBI submission process and improve the metadata quality of AIRR-seq studies.

CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata

BackgroundPublic biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources.ResultsThis work presents “CEDAR OnDemand”, a browser extension powered by the NCBO (National Center for Biomedical Ontology) BioPortal that enables users to seamlessly enter ontology-based metadata through existing web forms native to individual repositories. CEDAR OnDemand analyzes the web page contents to identify the text input fields and associate them with relevant ontologies which are recommended automatically based upon input fields’ labels (using the NCBO ontology recommender) and a pre-defined list of ontologies. These field-specific ontologies are used for controlling metadata entry. CEDAR OnDemand works for any web form designed in the HTML format. We demonstrate how CEDAR OnDemand works through the NCBI (National Center for Biotechnology Information) BioSample web-based metadata entry.ConclusionCEDAR OnDemand helps lower the barrier of incorporating ontologies into standardized metadata entry for public data repositories. CEDAR OnDemand is available freely on the Google Chrome store https://chrome.google.com/webstore/search/CEDAROnDemand