9,391 research outputs found

    Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

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    In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

    Evaluating the semantic web: a task-based approach

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    The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

    Semantic-driven matchmaking of web services using case-based reasoning

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    With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services

    NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

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    Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

    Ontology Ranking: Finding the Right Ontologies on the Web

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    Ontology search, which is the process of finding ontologies or ontological terms for users’ defined queries from an ontology collection, is an important task to facilitate ontology reuse of ontology engineering. Ontology reuse is desired to avoid the tedious process of building an ontology from scratch and to limit the design of several competing ontologies that represent similar knowledge. Since many organisations in both the private and public sectors are publishing their data in RDF, they increasingly require to find or design ontologies for data annotation and/or integration. In general, there exist multiple ontologies representing a domain, therefore, finding the best matching ontologies or their terms is required to facilitate manual or dynamic ontology selection for both ontology design and data annotation. The ranking is a crucial component in the ontology retrieval process which aims at listing the ‘relevant0 ontologies or their terms as high as possible in the search results to reduce the human intervention. Most existing ontology ranking techniques inherit one or more information retrieval ranking parameter(s). They linearly combine the values of these parameters for each ontology to compute the relevance score against a user query and rank the results in descending order of the relevance score. A significant aspect of achieving an effective ontology ranking model is to develop novel metrics and dynamic techniques that can optimise the relevance score of the most relevant ontology for a user query. In this thesis, we present extensive research in ontology retrieval and ranking, where several research gaps in the existing literature are identified and addressed. First, we begin the thesis with a review of the literature and propose a taxonomy of Semantic Web data (i.e., ontologies and linked data) retrieval approaches. That allows us to identify potential research directions in the field. In the remainder of the thesis, we address several of the identified shortcomings in the ontology retrieval domain. We develop a framework for the empirical and comparative evaluation of different ontology ranking solutions, which has not been studied in the literature so far. Second, we propose an effective relationship-based concept retrieval framework and a concept ranking model through the use of learning to rank approach which addresses the limitation of the existing linear ranking models. Third, we propose RecOn, a framework that helps users in finding the best matching ontologies to a multi-keyword query. There the relevance score of an ontology to the query is computed by formulating and solving the ontology recommendation problem as a linear and an optimisation problem. Finally, the thesis also reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques using real-world ontologies. This thesis will be useful for researchers and practitioners interested in ontology search, for methods and performance benchmark on ranking approaches to ontology search

    Oyster – Sharing and Re-using Ontologies in a Peer-to-Peer Community

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    In this paper, we present Oyster, a Peer-to-Peer system for exchanging ontology metadata among communities in the Semantic Web. Oyster exploits semantic web techniques in data representation, query formulation and query result presentation to provide an online solution for sharing ontologies, thus assisting researchers in re-using existing ontologies

    A Maut aprroach for reusing domain ontologies on the basis of the NeOn Methodlogy

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    Knowledge resource reuse has become a popular approach within the ontology engineering field, mainly because it can speed up the ontology development process, saving time and money and promoting the application of good practices. The NeOn Methodology provides guidelines for reuse. These guidelines include the selection of the most appropriate knowledge resources for reuse in ontology development. This is a complex decision-making problem where different conflicting objectives, like the reuse cost, understandability, integration workload and reliability, have to be taken into account simultaneously. GMAA is a PC-based decision support system based on an additive multi-attribute utility model that is intended to allay the operational difficulties involved in the Decision Analysis methodology. The paper illustrates how it can be applied to select multimedia ontologies for reuse to develop a new ontology in the multimedia domain. It also demonstrates that the sensitivity analyses provided by GMAA are useful tools for making a final recommendation

    Ontology (Science)

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    Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a _part of science_. Initial evidence for the truth of this proposition (which some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology develop¬ment work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the author¬ing, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists

    Experiment Databases: Creating a New Platform for Meta-Learning Research

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    Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results
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