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Helicobacter pylori, strain 26695, has a circular genome of 1,667,867 base pairs and 1,590 predicted coding sequences. Sequence analysis indicates that H. pylori has well-developed systems for motility, for scavenging iron, and for DNA restriction and modification. Many putative adhesins, lipoproteins and other outer membrane proteins were identified,(More)
Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic(More)
Measuring Inconsistency in ontologies is an important topic in ontology engineering as it can provide extra information for dealing with inconsistency. Many approaches have been proposed to deal with this issue. However, the main drawback of these algorithms is their high computational complexity. One of the main sources of the high complexity is the(More)
Consistent query answering over description logic-based ontologies is an important topic in ontol-ogy engineering as it can provide meaningful answers to queries posed over inconsistent ontologies. Current approaches for dealing with this problem usually consist of two steps: the first step is extracting some consistent sub-ontologies of an inconsistent(More)
Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the users' retrieval target especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the(More)
For the past few years, automatic Ontology construction and expansion is one of the most important research subjects in the field of knowledge engineering. Compared with the traditional Term Frequency method, we propose a semantics-based method to extract concepts from a large corpus of text documents and expand the concepts of the known Ontology based on(More)
With the existence of " semantic gap " between the machine-readable low level features (e.g. visual features in terms of colors and textures) and high level human concepts, it is inherently hard for the machine to automatically identify and retrieve events from videos according to their semantics by merely reading pixels and frames. This paper proposes a(More)