5,042 research outputs found

    Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

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    Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) 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 ICD, which is currently under active development by the WHO 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 how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD 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 subsequently change) 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.Comment: Published in the Journal of Biomedical Informatic

    Ontology Engineering

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    Organizational challenges of the semantic web in digital libraries

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    The Semantic Web initiative holds large promises for the future. There is, however, a considerable gap in Semantic Web research between the contributions in the technological field and the research done in the organizational field. This paper examines, from a socio-technical point of view the impact of Semantic Web technology on the strategic, organizational and technological levels. Building on a comprehensive case study at the National Library in Norway our findings indicate that the highest impact will be at the organizational level. The reason is mainly because inter-organizational and cross-organizational structures have to be established to address the problems of ontology engineering, and a development framework for ontology engineering in digital libraries must be examined

    Organisational challenges of the semantic web in digital libraries: A Norwegian case study

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    This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2009 Emerald Group Publishing LimitedPurpose – The purpose of this paper is to examine from a socio-technical point of view the impact of semantic web technology on the strategic, organisational and technological levels. The semantic web initiative holds great promise for the future for digital libraries. There is, however, a considerable gap in semantic web research between the contributions in the technological field and research in the organisational field. Design/methodology/approach – A comprehensive case study of the National Library of Norway (NL) is conducted, building on two major sources of information: the documentation of the digitising project of the NL; and interviews with nine different stakeholders at three levels of NL's organisation during June to August 2007. Top managers are interviewed on strategy, middle managers and librarians are interviewed regarding organisational issues and ICT professionals are interviewed on technology issues. Findings – The findings indicate that the highest impact will be at the organisational level. This is mainly because inter-organisational and cross-organisational structures have to be established to address the problems of ontology engineering, and a development framework for ontology engineering in digital libraries must be examined. Originality/value – ICT professionals and library practitioners should be more mindful of organisational issues when planning and executing semantic web projects in digital libraries. In particular, practitioners should be aware that the ontology engineering process and the semantic meta-data production will affect the entire organisation. For public digital libraries this probably will also call for a more open policy towards user groups to properly manage the process of ontology engineering

    Enhanced Integrated Scoring for Cleaning Dirty Texts

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    An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively.Comment: More information is available at http://explorer.csse.uwa.edu.au/reference

    An ontology co-design method for the co-creation of a continuous care ontology

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    Ontology engineering methodologies tend to emphasize the role of the knowledge engineer or require a very active role of domain experts. In this paper, a participatory ontology engineering method is described that holds the middle ground between these two 'extremes'. After thorough ethnographic research, an interdisciplinary group of domain experts closely interacted with ontology engineers and social scientists in a series of workshops. Once a preliminary ontology was developed, a dynamic care request system was built using the ontology. Additional workshops were organized involving a broader group of domain experts to ensure the applicability of the ontology across continuous care settings. The proposed method successfully actively engaged domain experts in constructing the ontology, without overburdening them. Its applicability is illustrated by presenting the co-created continuous care ontology. The lessons learned during the design and execution of the approach are also presented

    Constructive Ontology Engineering

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    The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated and semi-automated processes. Due to the vast amount of unstructured text and its continued proliferation, the problem of constructing ontologies from text has attracted considerable attention for research. However, the constructed ontologies may be noisy, with missing and incorrect knowledge. Thus ontology construction continues to be a challenging research problem. The goal of this research is to investigate a new method for guiding a process of extracting and assembling candidate terms into domain specific concepts and relationships. The process is part of an overall semi automated system for creating ontologies from unstructured text sources and is driven by the user’s goals in an incremental process. The system applies natural language processing techniques and uses a series of syntactical analysis tools for extracting grammatical relations from a list of text terms representing the parts of speech of a sentence. The extraction process focuses on evaluating the subject predicate-object sequences of the text for potential concept-relation-concept triples to be built into an ontology. Users can guide the system by selecting seedling concept-relation-concept triples to assist building concepts from the extracted domain specific terms. As a result, the ontology building process develops into an incremental one that allows the user to interact with the system, to guide the development of an ontology, and to tailor the ontology for the user’s application needs. The main contribution of this work is the implementation and evaluation of a new semi- automated methodology for constructing domain specific ontologies from unstructured text corpus
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