With the growing popularity of large-scale collaborative ontology-engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, we need new methods and insights to help project- and community-managers to cope with the constantly growing complexity of such projects. In this paper, we present a novel application of Markov chains to model sequential usage patterns that can be found in the change-logs of collaborative ontology-engineering projects. We provide a detailed presentation of the analysis process, describing all the required steps that are necessary to apply and determine the best fitting Markov chain model. Amongst others, the model and results allow us to identify structural properties and regularities as well as predict future actions based on usage sequences. We are specifically interested in determining the appropriate Markov chain orders which postulate on how many previous actions future ones depend on. To demonstrate the practical usefulness of the extracted Markov chains we conduct sequential pattern analyses on a large-scale collaborative ontology-engineering dataset, the International Classification of Diseases in its 11th revision. To further expand on the usefulness of the presented analysis, we show that the collected sequential patterns provide potentially actionable information for user-interface designers, ontology-engineering tool developers and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively engineering an ontology. We hope that presented work will spur a new line of ontology-development tools, evaluation-techniques and new insights, further taking the interactive nature of the collaborative ontology-engineering process into consideration. HighlightsWe provide a novel application for Markov.Using Markov chains we extract and analyze sequential usage patterns.We categorize the types of analyses that Markov chains enable us to perform.We demonstrate the utility of the Markov chain analysis on a large-scale project.
@cite_30: Building and maintaining thesauri are complex and laborious tasks. PoolParty is a Thesaurus Management Tool (TMT) for the Semantic Web, which aims to support the creation and maintenance of thesauri by utilizing Linked Open Data (LOD), text-analysis and easy-to-use GUIs, so thesauri can be managed and utilized by domain experts without needing knowledge about the semantic web. Some aspects of thesaurus management, like the editing of labels, can be done via a wiki-style interface, allowing for lowest possible access barriers to contribution. PoolParty can analyse documents in order to glean new concepts for a thesaurus. Additionally a thesaurus can be enriched by retrieving relevant information from Linked Data sources and thesauri can be imported and updated via LOD URIs from external systems and also can be published as new linked data sources on the semantic web.
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@cite_29: Enterprise modelling focuses on the construction of a structured description, the so-called enterprise model , which represents aspects relevant to the activity of an enterprise. Although it has become clearer recently that enterprise modelling is a collaborative activity, involving a large number of people, most of the enterprise modelling tools still only support very limited degrees of collaboration. Within this contribution we describe a tool for enterprise modelling, called MoKi (MOdelling wiKI), which supports agile collaboration between all different actors involved in the enterprise modelling activities. MoKi is based on a Semantic Wiki and enables actors with different expertise to develop an enterprise model not only using structural (formal) descriptions but also adopting more informal and semi-formal descriptions of knowledge.
@cite_3: We present OntoWiki, a tool providing support for agile, distributed knowledge engineering scenarios. OntoWiki facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWYG for text documents. It fosters social collaboration aspects by keeping track of changes, allowing to comment and discuss every single part of a knowledge base, enabling to rate and measure the popularity of content and honoring the activity of users. Ontowiki enhances the browsing and retrieval by offering semantic enhanced search strategies. All these techniques are applied with the ultimate goal of decreasing the entrance barrier for projects and domain experts to collaborate using semantic technologies. In the spirit of the Web 2.0 OntoWiki implements an ”architecture of participation” that allows users to add value to the application as they use it. It is available as open-source software and a demonstration platform can be accessed at http: 3ba.se.
@cite_44: In this paper, we present the GOSPL application that supports communities during the ontology engineering process by exploiting Social Web technologies and natural language. The resulting knowledge can then be transformed into RDF(S).
A number of tools, such as the OntoWiki @cite_3 , the MoKi @cite_29 , Soboleo @cite_44 or PoolParty @cite_30 support collaborative ontology engineering, focusing on supporting and augmenting different aspects of collaborative development processes of ontologies. For example, Semantic MediaWikis @cite_4 add semantic capabilities to traditional Wiki systems. They are intended to help users navigating the Wikis by introducing more meaningful semantic links and support of richer queries. Some of the Semantic Wikis available today focus on with semantic links in order to allow more meaningful navigation and to support richer queries. Semantic Wikis usually associate a page to a particular instance in the ontology, and the semantic annotations are converted into properties of that instance. As an ontology represents a formalized and abstract version of a specific domain, disagreements between authors on certain subjects can occur. Similar to face-to-face meetings, these collaborative ontology-engineering projects need tools that augment collaboration and help contributors in reaching consensus especially when modeling (controversial) topics of the real world.