Ontology Engineering Environments Research Articles

A Reference Architecture for Ontology Engineering Web Environments

Ontology authoring, maintenance and use are never easy tasks, mostly due to the complexity of real domains and how they dynamically change as well as different background possessed by modellers about methodologies and formal languages. However, although the needs for ontologies are well-understood, not less important is to provide editing tools to manipulate and understand them. In this context, this work proposes and documents a reference architecture for such tools running in web environments. Moreover, it provides the rationale for boosting the collaborative development of a novel tool based on this architecture, named crowd. Previous surveys reveal that few Web-based ontology engineering environments have been developed and in addition, almost all of them are mere visualisers, with limited graphical features and lacking inference services

Detecting User Profiles in Collaborative Ontology Engineering Using a User’s Interactions

Collaborative ontology-engineering methods usually prescribe a set of processes, activities, stakeholders, and the roles each stakeholder plays in these activities. We, however, believe that (a) the stakeholder community of each ontology-engineering project is different, and (b) one can observe different types of user behavior. It may thus very well be that the prescribed set of stakeholder types and roles do not suffice. If one were able to identify these user behavior types, which we will call user profiles, one can compliment or revisit those predefined roles. For instance, those user profiles can be used to provide customized interfaces for optimizing activities in certain ontology-engineering projects. We present a method for discovering different user profiles based on the interactions users have with each other on a collaborative ontology-engineering environment. Our approach clusters users based on the types of interactions they perform, which are retrieved from datasets that were annotated with an interaction ontology—built on top of SIOC—that we have developed. We demonstrate our method using the database of two instances of the GOSPL ontology-engineering tool. The databases contain the interactions of two distinct ontology-engineering projects involving, respectively, 42 and 36 users. For each dataset, we discuss the findings by analyzing the different clusters. We found that we are able to discover different user profiles, indicating that the approach we have taken is viable, though more experiments are needed to validate the results and to discover patterns across ontology-engineering projects.

Modelling expertise at different levels of granularity using semantic similarity measures in the context of collaborative knowledge-curation platforms.

Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements - or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP.

Human-centered ontology engineering: The HCOME methodology

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the Human Centered ONtology Engineering Environment, HCONE, which can effectively support this methodology.

Ontology learning for the Semantic Web

The Semantic Web relies heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. Thus, the proliferation of ontologies factors largely in the Semantic Web's success. The authors present an ontology learning framework that extends typical ontology engineering environments by using semiautomatic ontology construction tools. The framework encompasses ontology import, extraction, pruning, refinement and evaluation.