An Intelligent System Approach for Integrating Anatomical Ontologies

Abstract

ABSTRACTRecent years have seen a vast amount of data generated by various biological and biomedical experiments. The storage, management and analysis of this data, is done by means of the modern bioinformatics applications and tools. One of the bioinformatics instruments used for solving these tasks, are ontologies and the apparatus they provide. Ontology as a modeling tool is a specification of a conceptualization meaning that an ontology is a formal description of the concepts and relationships that can exist for a given software system or software agent (8, 10). Anatomical (phenotypic) ontologies of various species nowadays typically contain from few thousands to few tens of thousands of terms and relations (which is a very small number compared to the count of objects and the amount of data produced by biological experiments at the molecular level, for example) but usually the semantics employed in them is enormous in scale. The major problem when using such ontologies is that they lack intelligent tools for cross-species literature searches (text mining) as well as tools aiding the design of new biological and biomedical experiments with other (notyet tested) species/organisms, based on available information about experiments already performed on certain model species/organisms.This is where the process of merging anatomical ontologies comes into use. Using specific models and algorithms for merging of such ontologies is a matter of choice. In this work a novel approach for solving this task, based on two directed acyclic graph (DAG) models and three original algorithmic procedures is presented. Based on them, an intelligent software system for merging two (and possibly more) input/source anatomical ontologies into one output/target super-ontology was designed and implemented. This system was named AnatOM (an abbreviation from “Anatomical Ontologies Merger”).In this work a short overview of ontologies is provided describing what ontologies are and why they are widely used as a tool in bioinformatics. The problem of merging anatomical ontologies of two or more different organisms is introduced and some effort has been put into explaining why it is important. A general outline is presented of the models and the method that have been developed for solving the ontologies merging problem. A high-level overview of the AnatOM program implemented by the authors as part of this work is also provided.To achieve the degree of intelligence that is needed, the AnatOM program utilizes the large amount of high-quality data (knowledge) available in several widely popular and generally recognized knowledge bases such as UMLS, FMA, and WordNet. The last one of these is a general-purpose i.e. non-specialized knowledge source. The first two are biological/biomedical ones. Their choice was based on the fact that they provide a very good foundation for building an intelligent system that performs certain comparative anatomy tasks including mapping and merging of anatomical ontologies (23).