Ontology Engineering

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Robert Meersman - One of the best experts on this subject based on the ideXlab platform.

  • Advances in Web Semantics I - Ontology Engineering --- The DOGMA Approach
    Advances in Web Semantics I, 2008
    Co-Authors: Mustafa Jarrar, Robert Meersman
    Abstract:

    This chapter presents a methodological framework for Ontology Engineering (called DOGMA), which is aimed to guide Ontology builders towards building ontologies that are both highly reusable and usable, easier to build and to maintain. We survey the main foundational challenges in Ontology Engineering and analyse to what extent one can build an Ontology independently of application requirements at hand. We discuss Ontology reusability verses Ontology usability and present the DOGMA approach, its philosophy and formalization, which prescribe that an Ontology be built as separate domain axiomatization and application axiomatizations. While a domain axiomatization focuses on the characterization of the intended meaning (i.e. intended models) of a vocabulary at the domain level, application axiomatizations focus on the usability of this vocabulary according to certain application/usability perspectives and specify the legal models (a subset of the intended models) of the application(s)' interest. We show how specification languages (such as ORM, UML, EER, and OWL) can be effectively (re)used in Ontology Engineering.

  • Ontology Engineering the dogma approach
    Advances in Web Semantics I, 2008
    Co-Authors: Mustafa Jarrar, Robert Meersman
    Abstract:

    This chapter presents a methodological framework for Ontology Engineering (called DOGMA), which is aimed to guide Ontology builders towards building ontologies that are both highly reusable and usable, easier to build and to maintain. We survey the main foundational challenges in Ontology Engineering and analyse to what extent one can build an Ontology independently of application requirements at hand. We discuss Ontology reusability verses Ontology usability and present the DOGMA approach, its philosophy and formalization, which prescribe that an Ontology be built as separate domain axiomatization and application axiomatizations. While a domain axiomatization focuses on the characterization of the intended meaning (i.e. intended models) of a vocabulary at the domain level, application axiomatizations focus on the usability of this vocabulary according to certain application/usability perspectives and specify the legal models (a subset of the intended models) of the application(s)' interest. We show how specification languages (such as ORM, UML, EER, and OWL) can be effectively (re)used in Ontology Engineering.

  • an Ontology Engineering methodology for dogma
    Applied Ontology, 2008
    Co-Authors: Peter Spyns, Yan Tang, Robert Meersman
    Abstract:

    Although ontologies occupy a central place in the Semantic Web and related research domains, there are currently not many fully fledged Ontology Engineering methodologies available. In this paper, we want to present an integrated methodology for Ontology Engineering from scratch, inspired by various scientific disciplines, in particular database semantics and natural language processing.

  • context dependency management in Ontology Engineering a formal approach
    Journal on Data Semantics, 2007
    Co-Authors: Pieter De Leenheer, Aldo De Moor, Robert Meersman
    Abstract:

    A viable Ontology Engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and their converging and diverging interrelationships. Contexts are necessary to formalise and reason about such a dynamic wealth of knowledge. However, context dependencies introduce many complexities. In this article, we introduce a formal framework for supporting context dependency management processes, based on the DOGMA framework and methodology for scalable Ontology Engineering. Key notions are a set of context dependency operators, which can be combined to manage complex context dependencies like articulation, application, specialisation, and revision dependencies. In turn, these dependencies can be used in context-driven Ontology Engineering processes tailored to the specific requirements of collaborative communities. This is illustrated by a real-world case of interorganisational competency Ontology Engineering.

  • dogma mess a meaning evolution support system for interorganizational Ontology Engineering
    International Conference on Conceptual Structures, 2006
    Co-Authors: Aldo De Moor, Pieter De Leenheer, Robert Meersman
    Abstract:

    In this paper, we explore the process of interorganizational Ontology Engineering. Scalable Ontology Engineering is hard to do in interorganizational settings where there are many pre-existing organizational ontologies and rapidly changing collaborative requirements. A complex socio-technical process of Ontology alignment and meaning negotiation is therefore required. In particular, we are interested in how to increase the efficiency and relevance of this process using context dependencies between ontological elements. We describe the DOGMA-MESS methodology and system for scalable, community-grounded Ontology Engineering. We illustrate this methodology with examples taken from a case of interorganizational competency Ontology evolution in the vocational training domain.

Mark A. Musen - One of the best experts on this subject based on the ideXlab platform.

  • how to apply markov chains for modeling sequential edit patterns in collaborative Ontology Engineering projects
    International Journal of Human-computer Studies \ International Journal of Man-machine Studies, 2015
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    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.

  • discovering beaten paths in collaborative Ontology Engineering projects using markov chains
    Journal of Biomedical Informatics, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Tania Tudorache, Mark A. Musen
    Abstract:

    Display Omitted We model usage patterns of five different Ontology-Engineering projects.Users work in micro-workflows and specific user-roles can be identified.Class hierarchy influences users' edit behavior.Users edit ontologies top-down, breadth-first and prefer closely related classes.Users perform property-based workflows. Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based Ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, Ontology-Engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large Ontology-Engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical Ontology-Engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative Ontology-Engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, Ontology editors, developers and contributors working on collaborative Ontology-Engineering projects and tools in the biomedical domain.

  • Sequential Usage Patterns in Collaborative Ontology-Engineering Projects.
    arXiv: Human-Computer Interaction, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    With the growing popularity of large-scale biomedical collaborative Ontology-Engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, new methods and insights are needed 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 on the change-logs of collaborative Ontology-Engineering projects to extract and analyze sequential patterns. This method also allows to investigate memory and structure in human activity patterns when collaboratively creating an Ontology by leveraging Markov Chain models of varying orders. We describe all necessary steps for applying the methodology to collaborative Ontology-Engineering projects and provide first results for the International Classification of Diseases in its 11th revision. Furthermore, we show that the collected sequential-patterns provide actionable information for community- and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively Engineering an Ontology. We hope that the adaption of the presented methodology will spur a new line of Ontology-development tools and evaluation-techniques, which concentrate on the interactive nature of the collaborative Ontology-Engineering process.

  • developing crowdsourced Ontology Engineering tasks an iterative process
    CrowdSem'13 Proceedings of the 1st International Conference on Crowdsourcing the Semantic Web - Volume 1030, 2013
    Co-Authors: Jonathan M Mortensen, Mark A. Musen
    Abstract:

    It is increasingly evident that the realization of the Semantic Web will require not only computation, but also human contribution. Crowdsourcing is becoming a popular method to inject this human element. Researchers have shown how crowdsourcing can contribute to managing semantic data. One particular area that requires significant human curation is Ontology Engineering. Verifying large and complex ontologies is a challenging and expensive task. Recently, we have demonstrated that online, crowdsourced workers can assist with Ontology verification. Specifically, in our work we sought to answer the following driving questions: (1) Is crowdsourcing Ontology verification feasible? (2) What is the optimal formulation of the verification task? (3) How does this crowdsourcing method perform in an application? In this work, we summarize the experiments we developed to answer these questions and the results of each experiment. Through iterative task design, we found that workers could reach an accuracy of 88% when verifying SNOMED CT. We then discuss the practical knowledge we have gained from these experiments. This work shows the potential that crowdsourcing has to offer other Ontology Engineering tasks and provides a template one might follow when developing such methods.

  • how ontologies are made studying the hidden social dynamics behind collaborative Ontology Engineering projects
    Journal of Web Semantics, 2013
    Co-Authors: Markus Strohmaier, Simon Walk, Tania Tudorache, Jan Poschko, Daniel Lamprecht, Csongor Nyulas, Mark A. Musen
    Abstract:

    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 Ontology-evaluation 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 their construction is critical not only in understanding but consequently also in evaluating the ontologies. 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.

Simon Walk - One of the best experts on this subject based on the ideXlab platform.

  • how to apply markov chains for modeling sequential edit patterns in collaborative Ontology Engineering projects
    International Journal of Human-computer Studies \ International Journal of Man-machine Studies, 2015
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    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.

  • discovering beaten paths in collaborative Ontology Engineering projects using markov chains
    Journal of Biomedical Informatics, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Tania Tudorache, Mark A. Musen
    Abstract:

    Display Omitted We model usage patterns of five different Ontology-Engineering projects.Users work in micro-workflows and specific user-roles can be identified.Class hierarchy influences users' edit behavior.Users edit ontologies top-down, breadth-first and prefer closely related classes.Users perform property-based workflows. Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based Ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, Ontology-Engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large Ontology-Engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical Ontology-Engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative Ontology-Engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, Ontology editors, developers and contributors working on collaborative Ontology-Engineering projects and tools in the biomedical domain.

  • Sequential Usage Patterns in Collaborative Ontology-Engineering Projects.
    arXiv: Human-Computer Interaction, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    With the growing popularity of large-scale biomedical collaborative Ontology-Engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, new methods and insights are needed 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 on the change-logs of collaborative Ontology-Engineering projects to extract and analyze sequential patterns. This method also allows to investigate memory and structure in human activity patterns when collaboratively creating an Ontology by leveraging Markov Chain models of varying orders. We describe all necessary steps for applying the methodology to collaborative Ontology-Engineering projects and provide first results for the International Classification of Diseases in its 11th revision. Furthermore, we show that the collected sequential-patterns provide actionable information for community- and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively Engineering an Ontology. We hope that the adaption of the presented methodology will spur a new line of Ontology-development tools and evaluation-techniques, which concentrate on the interactive nature of the collaborative Ontology-Engineering process.

  • how ontologies are made studying the hidden social dynamics behind collaborative Ontology Engineering projects
    Journal of Web Semantics, 2013
    Co-Authors: Markus Strohmaier, Simon Walk, Tania Tudorache, Jan Poschko, Daniel Lamprecht, Csongor Nyulas, Mark A. Musen
    Abstract:

    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 Ontology-evaluation 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 their construction is critical not only in understanding but consequently also in evaluating the ontologies. 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.

Markus Strohmaier - One of the best experts on this subject based on the ideXlab platform.

  • how to apply markov chains for modeling sequential edit patterns in collaborative Ontology Engineering projects
    International Journal of Human-computer Studies \ International Journal of Man-machine Studies, 2015
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    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.

  • discovering beaten paths in collaborative Ontology Engineering projects using markov chains
    Journal of Biomedical Informatics, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Tania Tudorache, Mark A. Musen
    Abstract:

    Display Omitted We model usage patterns of five different Ontology-Engineering projects.Users work in micro-workflows and specific user-roles can be identified.Class hierarchy influences users' edit behavior.Users edit ontologies top-down, breadth-first and prefer closely related classes.Users perform property-based workflows. Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based Ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, Ontology-Engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large Ontology-Engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical Ontology-Engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative Ontology-Engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, Ontology editors, developers and contributors working on collaborative Ontology-Engineering projects and tools in the biomedical domain.

  • Sequential Usage Patterns in Collaborative Ontology-Engineering Projects.
    arXiv: Human-Computer Interaction, 2014
    Co-Authors: Simon Walk, Philipp Singer, Markus Strohmaier, Denis Helic, Mark A. Musen
    Abstract:

    With the growing popularity of large-scale biomedical collaborative Ontology-Engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, new methods and insights are needed 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 on the change-logs of collaborative Ontology-Engineering projects to extract and analyze sequential patterns. This method also allows to investigate memory and structure in human activity patterns when collaboratively creating an Ontology by leveraging Markov Chain models of varying orders. We describe all necessary steps for applying the methodology to collaborative Ontology-Engineering projects and provide first results for the International Classification of Diseases in its 11th revision. Furthermore, we show that the collected sequential-patterns provide actionable information for community- and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively Engineering an Ontology. We hope that the adaption of the presented methodology will spur a new line of Ontology-development tools and evaluation-techniques, which concentrate on the interactive nature of the collaborative Ontology-Engineering process.

  • how ontologies are made studying the hidden social dynamics behind collaborative Ontology Engineering projects
    Journal of Web Semantics, 2013
    Co-Authors: Markus Strohmaier, Simon Walk, Tania Tudorache, Jan Poschko, Daniel Lamprecht, Csongor Nyulas, Mark A. Musen
    Abstract:

    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 Ontology-evaluation 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 their construction is critical not only in understanding but consequently also in evaluating the ontologies. 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.

Markus Luczakrosch - One of the best experts on this subject based on the ideXlab platform.

  • peer production system or collaborative Ontology Engineering effort what is wikidata
    Proceedings of the 11th International Symposium on Open Collaboration, 2015
    Co-Authors: Claudia Mullerbirn, Benjamin Karran, Janette Lehmann, Markus Luczakrosch
    Abstract:

    Wikidata promises to reduce factual inconsistencies across all Wikipedia language versions. It will enable dynamic data reuse and complex fact queries within the world's largest knowledge database. Studies of the existing participation patterns that emerge in Wikidata are only just beginning. What delineates most of the contributions in the system has not yet been investigated. Is it an inheritance from the Wikipedia peer-production system or the proximity of tasks in Wikidata that have been studied in collaborative Ontology Engineering? As a first step to answering this question, we performed a cluster analysis of participants' content editing activities. This allowed us to blend our results with typical roles found in peer-production and collaborative Ontology Engineering projects. Our results suggest very specialised contributions from a majority of users. Only a minority, which is the most active group, participate all over the project. These users are particularly responsible for developing the conceptual knowledge of Wikidata. We show the alignment of existing algorithmic participation patterns with these human patterns of participation. In summary, our results suggest that Wikidata rather supports peer-production activities caused by its current focus on data collection. We hope that our study informs future analyses and developments and, as a result, allows us to build better tools to support contributors in peer-production-based Ontology Engineering.

  • collaborative Ontology Engineering a survey
    Knowledge Engineering Review, 2014
    Co-Authors: Elena Simperl, Markus Luczakrosch
    Abstract:

    Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern Ontology Engineering. This understanding of ontologies and Ontology Engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative Ontology Engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling Ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the Ontology Engineering team throughout the Ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing Ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative Ontology Engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising field

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