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