16,860 research outputs found
Database independent Migration of Objects into an Object-Relational Database
This paper reports on the CERN-based WISDOM project which is studying the
serialisation and deserialisation of data to/from an object database
(objectivity) and ORACLE 9i.Comment: 26 pages, 18 figures; CMS CERN Conference Report cr02_01
NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation
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
Class-Based Feature Matching Across Unrestricted Transformations
We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature. The transformation of the feature's appearance across viewing conditions is determined mainly by properties of the feature, rather than of the object in which it is embedded. Therefore, the transformed feature will be shared by approximately the same set of objects. Based on this consistency requirement, corresponding features can be reliably identified from a set of candidate matches. Unlike previous approaches, the proposed scheme compares feature appearances only in similar viewing conditions, rather than across different viewing conditions. As a result, the scheme is not restricted to locally planar objects or affine transformations. The approach also does not require examples of correct matches. We show that by using the proposed method, a dense set of accurate correspondences can be obtained. Experimental comparisons demonstrate that matching accuracy is significantly improved over previous schemes. Finally, we show that the scheme can be successfully used for invariant object recognition
The concordance of field-normalized scores based on Web of Science and Microsoft Academic data: A case study in computer sciences
In order to assess Microsoft Academic as a useful data source for evaluative
bibliometrics it is crucial to know, if citation counts from Microsoft Academic
could be used in common normalization procedures and whether the normalized
scores agree with the scores calculated on the basis of established databases.
To this end, we calculate the field-normalized citation scores of the
publications of a computer science institute based on Microsoft Academic and
the Web of Science and estimate the statistical concordance of the scores. Our
results suggest that field-normalized citation scores can be calculated with
Microsoft Academic and that these scores are in good agreement with the
corresponding scores from the Web of Science.Comment: 10 pages, 2 figures, 1 tabl
Subjects, Models, Languages, Transformations
Discussions about model-driven approaches tend to be hampered by terminological confusion. This is at least partially caused by a lack of formal precision in defining the basic concepts, including that of "model" and "thing being modelled" - which we call subject in this paper. We propose a minimal criterion that a model should fulfill: essentially, it should come equipped with a clear and unambiguous membership test; in other words, a notion of which subjects it models. We then go on to discuss a certain class of models of models that we call languages, which apart from defining their own membership test also determine membership of their members. Finally, we introduce transformations on each of these layers: a subject transformation is essentially a pair of subjects, a model transformation is both a pair of models and a model of pairs (namely, subject transformations), and a language transformation is both a pair of languages and a language of model transformations. We argue that our framework has the benefits of formal precision (there can be no doubt about whether something satifies our criteria for being a model, a language or a transformation) and minimality (it is hard to imagine a case of modelling or transformation not having the characterstics that we propose)
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