50,645 research outputs found
An ontology for software component matching
Matching is a central activity in the discovery and assembly of reusable software components. We investigate how ontology technologies can be utilised to support software component development. We use description logics, which underlie Semantic Web ontology languages such as OWL, to develop an ontology for matching requested and provided components. A link between modal logic and description logics will prove invaluable for the provision of reasoning support for component behaviour
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
An ontology for software component matching
The Web is likely to be a central platform for software development in the future. We investigate how Semantic Web technologies, in particular ontologies, can be utilised to support software component development in a Web environment. We use description logics, which underlie Semantic Web ontology languages such as DAML+OIL, to develop
an ontology for matching requested and provided components. A link between modal logic and description logics will prove invaluable for the provision of reasoning support for component and service behaviour
Embedding Non-Ground Logic Programs into Autoepistemic Logic for Knowledge Base Combination
In the context of the Semantic Web, several approaches to the combination of
ontologies, given in terms of theories of classical first-order logic and rule
bases, have been proposed. They either cast rules into classical logic or limit
the interaction between rules and ontologies. Autoepistemic logic (AEL) is an
attractive formalism which allows to overcome these limitations, by serving as
a uniform host language to embed ontologies and nonmonotonic logic programs
into it. For the latter, so far only the propositional setting has been
considered. In this paper, we present three embeddings of normal and three
embeddings of disjunctive non-ground logic programs under the stable model
semantics into first-order AEL. While the embeddings all correspond with
respect to objective ground atoms, differences arise when considering
non-atomic formulas and combinations with first-order theories. We compare the
embeddings with respect to stable expansions and autoepistemic consequences,
considering the embeddings by themselves, as well as combinations with
classical theories. Our results reveal differences and correspondences of the
embeddings and provide useful guidance in the choice of a particular embedding
for knowledge combination.Comment: 52 pages, submitte
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
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