Formalizing biomedical concepts from textual definitions

• Main Authors: Petrova, Alina, Ma, Yue, Tsatsaronis, George, Kissa, Maria, Distel, Felix, Baader, Franz, Schroeder, Michael
• Other Authors: BioMed Central,
• Format: Article
• Language: English
• Published: Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden 2016
• Subjects: Biomedizinische Ontologien; formale Definitionen; TU Dresden; Publikationsfonds; Biomedical ontologies; Formal definitions; MeSH; Relation extraction; SNOMED CT; Technical University Dresden; Publication funds; ddc:610; rvk:XA 10000
• Online Access: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-191181; http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-191181; http://www.qucosa.de/fileadmin/data/qucosa/documents/19118/s13326-015-0015-3.pdf

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.