Use artificial neural network to align biological ontologies

Jingshan Huang,Michael N Huhns,W Jim Zheng,Jiangbo Dang

doi:10.1186/1471-2164-9-s2-s16

Jingshan Huang, Michael N Huhns + Show 2 more

Open Access

https://doi.org/10.1186/1471-2164-9-s2-s16

Copy DOI

Abstract

BackgroundBeing formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.ResultsIn this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.ConclusionThe promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

Highlights

Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research
We present Ontology Alignment by Artificial Neural Network (OAANN), a new alignment algorithm to overcome some disadvantages of both rule-based and learning-based approaches
Our contributions are: (1) we exploit an approach to learning the weights for different semantic aspects of ontologies, through applying an artificial neural network technique during the ontology alignment; and (2) we tackle the difficult problem of carrying out machine learning techniques without help from instance data

Summary

Introduction

Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. Ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. The fields of biological and biomedical research are characterized by great complexity and imprecise terminologies. To address this imprecision and to standardize descriptions of biological entities, extensive efforts have been dedicated toward ontology development. To coordinate GO and other ontology development for biomedical research, the Open Biomedical Ontologies (OBO) group has developed mechanisms to share different ontologies [3]. Many ontologies in OBO have been represented in both the OBO format and Web Ontology Language (OWL) as well

Objectives

Methods

Results

Conclusion