7492825
Subdivision Interpolating Polygonization of Implicit Surfaces with Normal Meshes
Normal mesh is a new fundamental surface description, which is multiresolution mesh where each level can be written as a normal offset from a coarser version. In this paper, we present an algorithm to create normal subdivision triangulations approximating implicit surfaces. Our algorithm begins from a coarse base mesh created by a space-division based polygonization method for implicit surface, and the base mesh is then optimized by Laplacian smoothing operator. Subsequently, the faces of the base mesh are subdivided iteratively and the newly created vertices are located on the implicit surface along a certain direction, which depends on the normals of vertices of the faces. Finally, we obtain a piecewise-linear approximation of the surface, which is a normal mesh with subdivision connectivity and provides a multi-resolution description of the implicit surface naturally. Under the same error restriction, the mesh has much fewer data with respect to the traditional polygonization algorithms
207773102
Anisotropic Feature-Preserving Smoothing of 3D Mesh
4356074
A gauss function based approach for unbalanced ontology matching
Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.
 In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight ontology which represents the local domain knowledge, we extract a "similar" sub-ontology from the corresponding heavyweight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly calculate the influence values between concepts. In addition, we perform an extensive experiment to verify the effectiveness and efficiency of our proposed approach by using OAEI2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time.
4300885
RiMOM Results for OAEI 2009
In this report, we give a brief explanation of how RiMOM obtains the ontology alignment results at OAEI 2008 contest. We introduce the alignment process of RiMOM and more than 8 different alignment strategies integrated in RiMOM. Since every strategy is defined based on one specific ontological-information, we, in particular, study how the different strategies perform for different alignment tasks in the contest and design a strategy selection technique to get better performance. The result shows this technique is very useful. We also discuss some future work about RiMOM.
4332072
A Component Retrieval Method Based on Facet-Weight Self-learning
Component-based development method has been a new software development paradigm. How to get the needed components quickly and accurately is one of the basic problems about reusing software component automatically. In this paper, an intelligent component retrieval model – FWRM. is proposed. Facet presentation is used to model query and component. Multiple types of facets are defined which extends traditional keyword-based facet presentation. Genetic algorithm based facet weight self-learning algorithm can change the facet weight dynamically in order to improve retrieval accuracy. Corresponding similarity functions are defined also. In addition, risk minimization-based component sampling method is used to solve the insufficiency of training data. All these algorithms and methods are integrated into FWRM’s three main implementation parts: Facet-Weight Optimize System, Component Retrieve System and Resource. The experimental results prove that this method is feasible and can improve component retrieval effectively.
4341304
Result of Ontology Alignment with RiMOM at OAEI'06
In this report, we briefly describe our system RiMOM and its underlying techniques. Given two ontologies, RiMOM intends to combine multiple strategies, aiming at finding the "optimal" alignments from the source ontology to the target one. RiMOM integrates multiple strategies: edit-distance based strategy, statistical-learning based strategy, and three similarity-propagation based strategies. Each strategy is defined based on one kind of ontological-information/approach. RiMOM conducts alignment finding as follows. It first estimates two factors respectively approximately representing the structure similarity and the label similarity of the two ontologies. The two factors are used in strategy selection to determine which strategies will be used in the alignment task. Then, we apply the selected strategies to find the alignment independently and combine the alignment results. Finally we employ the alignment refinement to prune "unbelievable" alignments. This report presents our results based on the evaluation. We also share our thoughts on the experiment design, showing specific strengths and weaknesses of our approach.
4364549
Table Detection from Plain Text Using Machine Learning and Document Structure
Addressed in this paper is the issue of table extraction from plain text. Table is one of the commonest modes for presenting information. Table extraction has applications in information retrieval, knowledge acquisition, and text mining. Automatic information extraction from table is a challenge. Existing methods was mainly focusing on table extraction from web pages (formatted table extraction). So far the problem of table extraction on plain text, to the best of our knowledge, has not received sufficient attention. In this paper, unformatted table extraction is formalized as unformatted table block detection and unformatted table row identification. We concentrate particularly on the table extraction from Chinese documents. We propose to conduct the task of table extraction by combining machine learning methods and document structure. We first view the task as classification and propose a statistical approach to deal with it based on Naive Bayes. We define features in the classification model. Next, we use document structure to improve the detection performance. Experimental results indicate that the proposed methods can significantly outperform the baseline methods for unformatted table extraction.