@conference {IT701, title = {IT701: A Quality-Assurance Study of ChEBI}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Ontologies are important components of many health-information systems. The Chemical Entities of Biological Interest (ChEBI) ontology has become a standard reference for chemicals appearing in biological contexts. As such, assuring the quality of its content is imperative. In fact, ChEBI has a dedicated Web page at which errors and inconsistencies in its concepts can be reported. A study of the correctness of a random sample of ChEBI concepts is carried out. The results show that quite a large number of ChEBI concepts suffer from some kind of problematic modeling. For example, we found that 15.5\% of the sample concepts exhibited severe errors of commission, including incorrect hierarchical (is a) and lateral relationships. Errors of omission were also prevalent. The overall results of our quality-assurance (QA) study are presented. Suggestions for enhancing the QA processes in place for ChEBI are discussed.

}, url = {http://ceur-ws.org/Vol-1747/IT701_ICBO2016.pdf}, author = {Hasan Yumak and Ling Chen and Michael Halper and Ling Zheng and Yehoshua Perl and Gai Elhanan} } @conference {IT502, title = {IT502: Visualizing the {\textquotedblleft}Big Picture{\textquotedblright} of Change in NCIt{\textquoteright}s Biological Processes}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

The National Cancer Institute thesaurus (NCIt) is a large and complex ontology. NCIt is frequently updated; a new release is made available approximately every month. Tracking structural changes in NCIt is important for the editors of its content. In this paper we describe a methodology and tool using diff partial-area taxonomies to visually summarize structural changes between two NCIt releases. Diff partial-area taxonomies provide a comprehensible view of the overall impact of the changes. This methodology is illustrated using the Biological Process hierarchy. Specifically, we illustrate how diff partial-area taxonomies reflect change that occurred due to major restructuring of this hierarchy between September 2004 and December 2004. During this time the hierarchy nearly doubled in size and a large portion of the classes were extensively modified. Several kinds of change patterns are identified and discussed.

}, url = {http://ceur-ws.org/Vol-1747/IT502_ICBO2016.pdf}, author = {Yehoshua Perl and Christopher Ochs and Sherri de Coronado and Nicole Thomas} } @conference {IT501, title = {IT501: A Descriptive Delta for Identifying Changes in SNOMED CT}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

SNOMED CT is a large and complex medical terminology. Thousands of editing operations are applied to its content for each new release. Understanding what changed in a release is important for the end user and SNOMED CT editors. Each SNOMED CT release comes with release notes that provide a brief description of the changes that occurred and a set of delta files that identify individual changes in the content. The release notes are brief and changes to thousands of concepts may be described in a few sentences, whereas the delta files contain tens of thousands of individual changes. To better identify how SNOMED CT content changes between releases we introduce a methodology of creating a descriptive delta that captures the editing operations that were applied to SNOMED CT content in a given release in a more comprehensible form. We use this methodology to analyze editing operations that were part of a recent remodeling effort of the Congenital disease and Infectious disease subhierarchies in the large Clinical finding hierarchy.

}, url = {http://ceur-ws.org/Vol-1747/IT501_ICBO2016.pdf}, author = {Christopher Ochs and Yehoshua Perl and Gai Elhanan and James Case} } @conference {IP09, title = {IP09: How to Summarize Big Knowledge Subjects}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

One manifestation of the {\textquotedblleft}Big Knowledge{\textquoteright}{\textquoteright} challenge is providing automated tools for summarization of ontology content to facilitate user comprehension. An aggregation approach for the automatic identification and display of major subjects covered by an ontology{\textquoteright}s content is presented. The results show that our methodology is viable in capturing the {\textquotedblleft}big picture{\textquotedblright} of ontology content.

}, url = {http://ceur-ws.org/Vol-1747/IP09_ICBO2016.pdf}, author = {Ling Zheng and Yehoshua Perl and James Geller and Gai Elhanan} }