Fast modularisation and aomic decomposition of ontologies using axiom dependency hypergraphs

In this paper we define the notion of an axiom dependency hypergraph, which explicitly represents how axioms are included into a module by the algorithm for computing locality-based modules. A locality-based module of an ontology corresponds to a set of connected nodes in the hypergraph, and atoms of an ontology to strongly connected components. Collapsing the strongly connected components into single nodes yields a condensed hypergraph that comprises a representation of the atomic decomposition of the ontology. To speed up the condensation of the hypergraph, we first reduce its size by collapsing the strongly connected components of its graph fragment employing a linear time graph algorithm. This approach helps to significantly reduce the time needed for computing the atomic decomposition of an ontology. We provide an experimental evaluation for computing the atomic decomposition of large biomedical ontologies. We also demonstrate a significant improvement in the time needed to extract locality-based modules from an axiom dependency hypergraph and its condensed version

An empirically-based framework for ontology modularization

Modularity is being increasingly used as an approach to solve for the information overload problem in ontologies. It eases cognitive complexity for humans, and computational complexity for machines. The current literature for modularity focuses mainly on techniques, tools, and on evaluation metrics. However, ontology developers still face difficulty in selecting the correct technique for specific applications and the current tools for modularity are not sufficient. These issues stem from a lack of theory about the modularisation process. To solve this problem, several researchers propose a framework for modularity, but alas, this has not been realised, up until now. In this article, we survey the existing literature to identify and populate dimensions of modules, experimentally evaluate and characterise 189 existing modules, and create a framework for modularity based on these results. The framework guides the ontology developer throughout the modularisation process. We evaluate the framework with a use-case for the Symptom ontology

Improved Algorithms for Module Extraction and Atomic Decomposition

Abstract. In recent years modules have frequently been used for ontology development and understanding. This happens because a module captures all the knowledge an ontology contains in a given area, and often is much smaller than the whole ontology. One useful modularisation technique for expressive ontology languages is locality-based modularisation, which allows for fast (polynomial) extraction of modules. In order to better understand the modular structure of an ontology, a technique called Atomic Decomposition can be used. It efficiently builds a structure representing all possible modules for an ontology, regardless of the modularisation algorithm adopted and without the need to compute an exponential number of modules, as in a naive approach. This structure may be used e.g., for quick extraction of modules, or to investigate dependencies between modules, and so on. However, existing algorithms for both locality-based module extraction and atomic decomposition do not scale well. This happens mainly because of their global nature: each iteration always explores the whole ontology, even when it is not necessary. We propose algorithms for locality-based module extraction and atomic decomposition that work only on the relevant part of the ontology. This improves performance of algorithms by avoiding unnecessary checks. Empirical evaluation confirms a significant speed up on real-life ontologies.