Towards ontology evaluation across the life cycle

Currently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated. Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies. This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology.Currently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated. Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies. This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology. This document focuses on the evaluation of five aspects of the quality of ontologies: intelligibility, fidelity, craftsmanship, fitness, and deployability. A model for the ontology life cycle is presented, and evaluation criteria are presented in the context of the phases of the life cycle. We discuss the availability of tools and the document ends with observations and recommendations. Given the current level of maturity of ontology as an engineering discipline, any results on how to best build and evaluate ontologies have to be considered as preliminary. However, the results achieved a broad consensus across the range of backgrounds, application foci, specialties and experience found in the Ontology Summit community.Fil: Neuhaus, Fabian.Fil: Vizedom, Amanda.Fil: Baclawski, Ken.Fil: Bennett, Mike.Fil: Denny, Michael.Fil: Grüninger, Michael.Fil: Hashemi, Ali.Fil: Longstreth, Terry.Fil: Hashemi, Ali.Fil: Obrst, Leo.Fil: Ray, Steve.Fil: Sriram, Ram.Fil: Schneider, Todd.Fil: Vegetti, Maria Marcela.Fil: West, Matthew.Fil: Yim, Peter

Semantic Web and Big Data meets Applied Ontology

The role that ontologies play or can play in designing and employing semantic technologies has been widely acknowledged by the Semantic Web and Linked Data communities. But the level of collaboration between these communities and the Applied Ontology community has been much less than expected. And ontologies and ontological techniques appear to be of marginal use in Big Data and its applications. To understand this situation and foster greater collaboration, Ontology Summit 2014 brought together representatives from the Semantic Web, Linked Data, Big Data and Applied Ontology communities, to address three basic problems involving applied ontology and these communities: (1) The role of ontologies [in these communities], (2) Current uses of ontologies in these communities, and (3) Engineering of ontologies and semantic integration. The intent was to identify and understand: (a) causes and challenges (e.g. scalability) that hinder reuse of ontologies in SW and LD, (b) solutions that can reduce the differences between ontologies on and off line, and (c) solutions to overcome engineering bottlenecks in current Semantic Web and Big Data applications. Over the past four months, presentations from, and discussions with, representatives of the Semantic Web, Linked Data, and Applied Ontology communities have taken place across four tracks. Each Track focused on different aspects of this year?s Summit topic: (Track A) Investigation of sharable and reusable ontologies; (Track B) Tools, services and techniques for a comprehensive and effective use of ontologies; (Track C) Investigation of the engineering bottlenecks and the ways to prevent and overcome them; (Track D) Enquiry on the variety problem in Big Data. In addition to the four Tracks? activities there was a Hackathon. Six different Hackathon projects took place, all available at their individual project public repositories. An online Community Library and an online Ontology Repository have been created as freely accessible Community resources. This Ontology Summit 2014 Communique presents a summary of the results, original in its attempt both to merge different communities? discourses and to achieve consensus across the Summit participants with respect to open problems and recommendations to address them.Fil: Obrst, Leo. The MITRE Corporation; Estados UnidosFil: Gruninger, Michael. University Of Toronto; CanadáFil: Baclawski, Ken. Northeastern University; Estados UnidosFil: Bennett, Mike. Hypercube Ltd.; Reino UnidoFil: Brickley, Dan. Google; Reino UnidoFil: Berg Cross, Gary. Knowledge Strategies; Estados UnidosFil: Hitzler, Pascal. Wright State University; Estados UnidosFil: Janowicz, Krzysztof. University of California; Estados UnidosFil: Kapp, Christine. Hypercube Ltd; Estados UnidosFil: Kutz, Oliver. Otto von Guericke University Magdeburg; AlemaniaFil: Lange, Christoph. University of Bonn; AlemaniaFil: Levenchuk, Anatoly. TechInvestLab.ru; RusiaFil: Quattri, Francesca. The Hong Kong Polytechnic University; Hong KongFil: Rector, Alan. University Of Manchester; Reino UnidoFil: Schneider, Todd. PDS, Inc.; Estados UnidosFil: Spero, Simon. University Of North Carolina; Estados UnidosFil: Thessen, Anne. Arizona State University; Estados UnidosFil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Vizedom, Amanda. Criticollab; Estados UnidosFil: Westerinen, Andrea. Nine Points Solutions; Estados UnidosFil: West, Matthew. Information Junction; Reino UnidoFil: Yim, Peter. CIM Engineering, Inc.; Estados Unido