Ontology similarity in the alignment space

david2010bInternational audienceMeasuring similarity between ontologies can be very useful for different purposes, e.g., finding an ontology to replace another, or finding an ontology in which queries can be translated. Classical measures compute similarities or distances in an ontology space by directly comparing the content of ontologies. We introduce a new family of ontology measures computed in an alignment space: they evaluate the similarity between two ontologies with regard to the available alignments between them. We define two sets of such measures relying on the existence of a path between ontologies or on the ontology entities that are preserved by the alignments. The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. All these measures have been implemented in the OntoSim library, that has been used in experiments which showed that entity preserving measures are comparable to the best ontology space measures. Moreover, they showed a robust behaviour with respect to the alteration of the alignment space

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them

The Role of Platforms for Enterprise Ecosystems

Workshop Editoria

Representation Language-Neutral Modeling of Ontologies

In this paper we present a new approach for language-neutral modeling of large-scale ontologies. The gist of our approach lies in the way we treat the majority of axioms. Instead of capturing axiom semantics in some specific representation language, we categorize axioms into different types and specify them as complex objects that refer to concepts and relations. A separate layer that is language-specific, in fact it may even vary for different inference engines working on the same language, describes how these objects are translated into a target representation. In addition to its far reaching independence with regard to specific representation languages, this approach benefits engineering since the semantics of important types of axioms may be much more elucidated in our ontology engineering tool, OntoEdit, than in comparable tools. Furthermore, our approach is principled in a way that allows for comparably easy adaptation of our tool to requirements for modeling axioms in specific d..

Representation Language-Neutral Modeling of Ontologies

Abstract In this paper we present a new approach for language-neutral modeling of large-scale ontologies. The gist of our approach lies in the way we treat the majority of axioms. Instead of capturing axiom semantics in some specific representation language, we categorize axioms into different types and specify them as complex objects that refer to concepts and relations. A separate layer that is language-specific, in fact it may even vary for different inference engines working on the same language, describes how these objects are translated into a target representation. In addition to its far reaching independence with regard to specific representation languages, this approach benefits engineering since the semantics of important types of axioms may be much more elucidated in our ontology engineering tool, OntoEdit, than in comparable tools. Furthermore, our approach is principled in a way that allows for comparably easy adaptation of our tool to requirements for modeling axioms in specific domains. The apparition of these faces in the crowd; Petals on a wet black bough. Ezra Pound, 1913

Comparison between ontology distances (preliminary results)

david2008aInternational audienceThere are many reasons for measuring a distance between ontologies. In particular, it is useful to know quickly if two ontologies are close or remote before deciding to match them. To that extent, a distance between ontologies must be quickly computable. We present constraints applying to such measures and several possible ontology distances. Then we evaluate experimentally some of them in order to assess their accuracy and speed