Cicero: Tracking Design Rationale in Collaborative Ontology Engineering

Summary. Existing mature ontology engineering approaches are based on some basic assumptions that are often neglected in practice.Ontologies often need to be built in a decentralized way, ontologies must be given to a community in a way such that individuals have partial autonomy over them, ontologies have a life cycle that involves an iteration back and forth between construction/modification and use and ontologies should support the participation of non-expert users in ontology engineering processes.While recently there have been some initial proposals to consider these issues, they lack the appropriate rigor of mature approaches. i.e. these recent proposals lack the appropriate depth of methodological description, which makes the methodology usable, and they lack a proof of concept by concrete cases studies. In this paper, we describe the DILIGENT methodology that takes decentralization, partial autonomy, iteration and non-expert builders into account and we demonstrate its proof-ofconcept in two real-world organizational case studies.

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“…Moreover it also shows us that proper social management procedures and tool support helps to reach consensus in a smoother way (cf. [2]). …”

Section: Developing the Diligent Ontology Engineering Methodologymentioning

confidence: 99%

Ontology Engineering and Evolution in a Distributed World Using DILIGENT

Pinto

Tempich

Staab

2009

Handbook on Ontologies

Self Cite

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“…Dellschaft et al, [5] has proposed a strategy of Cicero tool which enhances the convenience of ontology authoring. The underlying methodology in this approach is that the facility to provide more documentation while ontology authoring such that the actual meaning of ontology is clearly portrayed in this approach.…”

Section: Related Literaturementioning

confidence: 99%

A Hybridized Framework for Ontology Modeling incorporating Latent Semantic Analysis and Content based Filtering

N¹,

Deepak²,

J³

et al. 2016

IJCA

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In the era of Semantic Web, organization of the necessary Semantic Information becomes quite vital for improving overall retrieval efficiency of the Semantic Web contents. Ontologies are one of the most important and yet the most primary entities of the semantic web which is used for representing and modeling knowledge. Authoring of ontologies must be done in a highly systematic and an organized manner in order to validate the correctness of the ontologies authored. Several traditional ontology authoring systems are based on Semantic Wikis which use graphs to store the ontological entities that increase the overall complexity of ontologies which needs to be overcome. A Hash Table based ontology organization strategy is proposed which is further empowered by a Semantic Latent Analysis to compute the ontological relevance. Several agents are incorporated to check the correctness of ontologies. The proposed framework is further enhanced with Content Based Filtering for yielding better results. The proposed methodology yields an accuracy percentage of 88.99.

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“…In 2007 Tempich et al [26] conducted a detailed case study using DILIGENT to create an ontology. CICERO [27], which was introduced in 2008 and is an extension to the Semantic Media Wiki 2 , follows a similar approach and augments user discussions and documentation as well as efficiency by supporting the design rationale of ontology engineers and is also based on DILIGENT. However, once an ontology is engineered it still needs to be maintained, meaning that already existing concepts and properties have to be updated.…”

Section: Collaborative Ontology Engineeringmentioning

confidence: 99%

How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects

et al. 2013

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Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontologyevaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to its construction is critical not only in understanding but consequently also in evaluating the ontology. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.

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Cicero: Tracking Design Rationale in Collaborative Ontology Engineering

Cited by 23 publications

References 4 publications

Ontology Engineering and Evolution in a Distributed World Using DILIGENT

Ontology Engineering and Evolution in a Distributed World Using DILIGENT

A Hybridized Framework for Ontology Modeling incorporating Latent Semantic Analysis and Content based Filtering

How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects

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