Ontologies are at the heart of the semantic web. They define the concepts and relationships that make global interoperability possible. However, as these ontologies grow in size they become more and more difficult to create, use, understand, maintain, transform and classify. We present and evaluate several algorithms for extracting relevant segments out of large description logic ontologies for the purposes of increasing tractability for both humans and computers. The segments are not mere fragments, but stand alone as ontologies in their own right. This technique takes advantage of the detailed semantics captured within an OWL ontology to produce highly relevant segments. The research was evaluated using the GALEN ontology of medical terms and procedures.

One of the main hurdles towards a wide endorsement of ontologies is the high cost of constructing them. Reuse of existing ontologies offers a much cheaper alternative than building new ones from scratch, yet tools to support such reuse are still in their infancy. However, more ontologies are becoming available on the web, and online libraries for storing and indexing ontologies are increasing in number and demand. Search engines have also started to appear, to facilitate search and retrieval of online ontologies. This paper presents a fresh view on constructing ontologies automatically, by identifying, ranking, and merging fragments of online ontologies.

The requirements of specific applications and services are often over estimated when ontologies are designed and built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services, and investigates the possibility of relying on this usage information to winnow that ontology.

Many ontologies are built for the main purpose of representing a domain, rather than to meet the requirements of a specific application. When applications and services are deployed over an ontology, it is sometimes the case that only few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could useful for realising the necessary fragments of the ontology to run the applications. Such information could be used to winnow an ontology, i.e., simplifying or shrinking the ontology to smaller, more fit for purpose sizes. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services, and investigate the possibility of using this information to winnow that ontology. 1

Nowadays, the increasing amount of semantic data available on the Web leads to a new stage in the potential of Semantic Web applications. However, it also introduces new issues due to the heterogeneity of the available semantic resources. One of the most remarkable is redundancy, that is, the excess of different semantic descriptions, coming from different sources, to describe the same intended meaning. In this paper, we propose a technique to perform a large scale integration of senses (expressed as ontology terms), in order to cluster the most similar ones, when indexing large amounts of online semantic information. It can dramatically reduce the redundancy problem on the current Semantic Web. In order to make this objective feasible, we have studied the adaptability and scalability of our previous work on sense integration, to be translated to the much larger scenario of the Semantic Web. Our evaluation shows a good behaviour of these techniques when used in large scale experiments, then making feasible the proposed approach.

Abstract. Sub-Ontology extraction, from a large ontology, leads to the generation of a specialized knowledge model that is pertinent to specific problems. Existing sub-ontology extraction methods tend to either render a too generalized or a too restricted sub-ontology that at times does notcapturetheentiresemanticsoftheparentontology.Wepresentasubontology extraction method that using N3 rules to extract a sub-ontology from RDF(S) and OWL ontologies whilst extending the semantics of the extracted concepts and their relationships in the sub-ontology. Our approach features the following tenets (i) identifying the user-selected concepts; (ii) extracting the user-selected concepts, their roles and their individuals; and (iii) extracting other concepts, roles and individuals that are immediate structurally-connected with the user-selected concepts. As a test-case,wepresentthesub-ontologyextractionoutcomeforahealthcare ontologyrepresentingProstateCancer caremanagement. 1

and other research outputs Ontology construction from online ontologies

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Abstract. We introduce ontological blending as a method for combining ontologies. Compared with existing combination techniques that aim at integrating or assimilating categories and relations of thematically related ontologies, blending aims at creatively generating (new) categories and ontological definitions; this is done on the basis of input ontologies whose domains are thematically distinct but whose specifications share structural or logical properties. As a result, ontological blending can generate new ontologies and concepts and it allows a more flexible technique for ontology combination compared to existing methods. Our approach to computational creativity in conceptual blending is inspired by methods rooted in cognitive science (e.g., analogical reasoning), ontological engineering, and algebraic specification. Specifically, we introduce the basic formal definitions for ontological blending, and show how the distributed ontology language DOL (currently being standardised within the OntoIOp—Ontology Integration and Interoperability—activity of ISO/TC 37/SC 3) can be used to declaratively specify blending diagrams. 1