Ontology evaluation is a maturing discipline with methodologies and measures being developed and proposed. However, evaluation methods that have been proposed have not been applied to specific examples. In this paper, we present the state-of-the-art in ontology evaluation- current methodologies, criteria and measures, analyse appropriate evaluations that are important to our application- browsing in Wikipedia, and apply these evaluations in the context of ontologies with varied properties. Specifically, we seek to evaluate ontologies based on categories found in Wikipedia. Categories and Subject Descriptors

Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applications on ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system. 1

The design and maintenance of ontologies is a complex social collaborative activity, and this is true especially for semantic-web ontologies. On the one hand, such activity calls for the availability of tools providing support to typical operations such as the reuse of existing ontologies and design patterns, the re-engineering of thesauri, lexicons, folksonomies, database schemas, and knowledge from corpora, or to the appropriate evaluation and selection processes which are needed in order to make an ontology functional to a given task. On the other hand, tools able to support the collaborative performance of all these operations, aiding e.g. the discussion and consensus-reaching processes on an ontology element and its rationale, should be provided too. Current tools substantially fail to address both types of need. In our opinion, this is partly due to the lack of both an adequate requirement analysis, which describes the actual processes and data that are usually managed during ontology-design activities, and a unifying conceptual framework, which puts together the several interrelated aspects of (collaborative) ontology design. In this paper we present a formal framework that represents the notions needed to express requirements for the development of collaborative ontology engineering tools. The framework is formalized as an OWL(DL) ontology named C-ODO (Collaborative Ontology-Design Ontology), and is being used within the EU NeOn project. 1.

D3.1b Ontology validation on the basis of (multilingual) search – first cycle Due date of deliverable: 30-11-2007 Actual submission date: 21-12-2007 Start date of project: 1-12-2005 Duration: 30 Months Organisation name of lead contractor for this deliverable: Bulgarian Academy of Sciences (IPP-

Abstract. The process of authoring ontologies requires the active involvement of domain experts who should lead the process, as well as providing the relevant conceptual knowledge. However, most domain experts lack knowledge modelling skills and find it hard to follow logical notations in OWL. This paper presents ROO, a tool that facilitates domain experts ' definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit. ROO guides users through the ontology construction process by following a methodology geared towards domain experts ’ involvement in ontology authoring, and exploiting intelligent user interfaces techniques. An evaluation study has been conducted comparing ROO against another popular ontology authoring tool. Participants were asked to create ontologies based on hydrology and environment modelling scenarios related to real tasks at the mapping agency of Great Britain. The study is discussed, focusing on the usability and usefulness of the tool, and the quality of the resultant ontologies.

Abstract. Information Retrieval (IR) research has recently started addressing the information need of exploratory search. where the searcher may be unfamiliar with the domain or not have decided what is the goal of his query. A popular tool to support exploratory search is the use of faceted search. The implementation of faceted search requires that documents be annotated by metadata in the form of attributes and hierarchical categories. In many applications, the metadata is maintained manually, in the form of a search ontology. Recent work has also investigated methods to automatically acquire such metadata from sample documents [1, 2]. In this work, we propose a new method to automatically evaluate the quality of such a search ontology. Our method relies on mapping ontology instances to textual documents. On the basis of this mapping, we evaluate the adequacy of ontology relations by measuring their classification potential over the textual documents. This data-driven method provides concrete feedback to ontology

Abstract: The Semantic Web aims at evolving the ‘web of data ’ to an ‘intelligent information space ’ which is responsive to both human beings and computer systems. Consequently, semantic technologies have emerged in many application fields, primarily to provide intelligent IT systems. Semantics, therefore, is considered to be the essence of systemic intelligence. In this paper, we introduce a lifecycle model for semantics, consisting of five phases: a modelling b application c authoring d mining e evaluation of semantic information. With respect to this model, we analyse semantic lifecycles from former research work and summarise experiences as well as future research activities.

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Ontologies enjoy increasing popularity among bioinformatics researchers who seek assistance in coping with the rapid upgrowth of biological data to be handled and related information to be considered. While communities of committed ontology pioneers drive the development and optimisation of ontologies for a wide range of relevant domains, the amble adoption of ontologies in bioinformatics research is still decelerated by technological, managerial and communication barriers. This paper contains a brief review of current trends, challenges and perspectives in ontology research and practice in bioinformatics.