Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
BMC Bioinformatics. 2018 Jan 5;19(1):7. doi: 10.1186/s12859-017-1999-8.
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge.
We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .
Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
本体是对领域概念化的表示。传统上，生物学中的本体被表示为有向无环图（DAG），它表示骨干分类法和类之间的其他关系。这些图被广泛用于以本体丰富或语义相似性计算的形式进行数据分析。最近，本体以形式语言（如 Web 本体语言（OWL））进行开发，由一组公理组成，通过这些公理定义或约束类。虽然本体的分类法可以直接从本体的公理中推断出来，作为标准 OWL 推理任务之一，但从 OWL 本体创建利用本体语义内容的通用图形结构仍然是一个挑战。
我们开发了一种使用自动推理器将本体转换为图形的方法，同时考虑类之间的所有关系。通过在本体的演绎闭包中搜索（存在）模式，我们可以识别隐含但未断言的类之间的关系，并生成图形结构，该图形结构对本体的语义内容的很大一部分进行编码。我们通过将其应用于通过语义相似性推断基因本体论中的蛋白质-蛋白质相互作用来证明我们方法的优势，并证明当根据我们的方法使用演绎推理推断图形结构时，性能会提高。我们的软件和实验结果可在 http://github.com/bio-ontology-research-group/Onto2Graph 上获得。
Onto2Graph 是一种使用自动推理从 OWL 本体生成图形结构的方法。生成的图形可用于改进本体可视化和基于本体的数据分析。
