Browsing by Subject "ontologies"

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    A Biological and Bioinformatics Ontology for Service Discovery and Data Integration
    (2006-07-26T15:44:50Z) Dippold, Mindi M.; Mahoui, Malika
    This project addresses the need for an increased expressivity and robustness of ontologies already supporting BACIIS and SIBIOS, two systems for data and service integration in the life sciences. The previous ontology solutions as global schema and facilitator of service discovery sustained the purposes for which they were built to provide, but were in need of updating in order to keep up with more recent standards in ontology descriptions and utilization as well as increase the breadth of the domain and expressivity of the content. Thus, several tasks were undertaken to increase the worth of the system ontologies. These include an upgrade to a more recent ontology language standard, increased domain coverage, and increased expressivity via additions of relationships and hierarchies within the ontology as well as increased ease of maintenance by a distributed design.
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    Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms
    (2015) Xiang, Yang; Janga, Sarath Chandra
    The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, available works on ontology integration are largely heuristic without guarantees on the quality of the integration results. In this work, we focus on the integration of ontologies with hierarchical structures. We identified optimal structures in this problem and proposed optimal and efficient approximation algorithms for integrating a pair of ontologies. Furthermore, we extend the basic problem to address the integration of a large number of ontologies, and correspondingly we proposed an efficient approximation algorithm for integrating multiple ontologies. The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches. In addition, the results of integration between gene ontology and National Drug File Reference Terminology suggest that our method provides a novel way to perform association studies between biomedical terms.
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    Evaluating Methods for Identifying Cancer in Free-Text Pathology Reports Using Various Machine Learning and Data Preprocessing Approaches
    (IOS, 2015) Kasthurirathne, Suranga Nath; Dixon, Brian E.; Grannis, Shaun J.; Department of BioHealth Informatics, School of Informatics and Computing
    Automated detection methods can address delays and incompleteness in cancer case reporting. Existing automated efforts are largely dependent on complex dictionaries and coded data. Using a gold standard of manually reviewed pathology reports, we evaluated the performance of alternative input formats and decision models on a convenience sample of free-text pathology reports. Results showed that the input format significantly impacted performance, and specific algorithms yielded better results for presicion, recall and accuracy. We conclude that our approach is sufficiently accurate for practical purposes and represents a generalized process.