4370569
Using Bayesian decision for ontology mapping
Ontology mapping is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. Many efforts have been conducted to automate the discovery of ontology mapping. However, some problems are still evident. In this paper, ontology mapping is formalized as a problem of decision making. In this way, discovery of optimal mapping is cast as finding the decision with minimal risk. An approach called Risk Minimization based Ontology Mapping (RiMOM) is proposed, which automates the process of discoveries on 1:1, n:1, 1:null and null:1 mappings. Based on the techniques of normalization and NLP, the problem of instance heterogeneity in ontology mapping is resolved to a certain extent. To deal with the problem of name conflict in mapping process, we use thesaurus and statistical technique. Experimental results indicate that the proposed method can significantly outperform the baseline methods, and also obtains improvement over the existing methods.
4317013
Verification of Web Service Conversations Specified in WSCL
This paper studies one of the standards about Web service conversations, the WSCL specification. We propose a method to verify conversations in WSCL. In this method we first propose using a translator to convert WSCL documents into promela, the modeling language of the model checker SPIN. Then, we run SPIN to check the conversation model against the correctness properties specified by the designer. A toolkit for WSCL verification is introduced at the end of this paper.
4985005
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in many real-world applications, in particular with the rapid growth of the Web 2.0, ranking over multiple interrelated (heterogeneous) domains becomes a common situation, where in some domains we may have a large amount of training data while in some other domains we can only collect very little. One important question is: "if there is not sufficient supervision in the domain of interest, how could one borrow labeled information from a related but heterogenous domain to build an accurate model?". This paper explores such an approach by bridging two heterogeneous domains via the latent space. We propose a regularized framework to simultaneously minimize two loss functions corresponding to two related but different information sources, by mapping each domain onto a "shared latent space", capturing similar and transferable oncepts. We solve this problem by optimizing the convex upper bound of the non-continuous loss function and derive its generalization bound. Experimental results on three different genres of data sets demonstrate the effectiveness of the proposed approach.
4337414
Keyword Extraction Based Peer Clustering
Peer clustering plays an important role in P2P systems like peer dis- covery, resource sharing and management, etc. Keywords provide rich semantic information about the peers' interests. Keyword extraction from documents is a useful method in topic retrieval and document clustering. Peers exchange re- sources and some of them are text documents like news and novels. Such documents represent the interests of a peer. This paper proposes a method for clustering peers using the exchange text documents between them. The docu- ments are used for keyword extraction. The keyword extraction is treated as a decision problem and based on Bayesian decision theory. The peers' similari- ties can be calculated by keyword similarities. And the cluster method is based on the peers' similarities. The experiment gives satisfied results. Finally, the conclusion is discussed.
5652709
Multiple strategies detection in ontology mapping
Ontology mapping is the task of finding semantic relationships between entities (i.e. concept, attribute and relation) of two ontologies. In the existing literatures, many (semi-)automatic approaches have found considerable interest by combining several mapping strategies (namely multi-strategy mapping). However, experiments show that multi-strategy based mapping does not always outperform its single-strategy counterpart. We here mainly consider the following questions: For a new, unseen mapping task, should one use a multi-strategy or a single-strategy? And if the task is suitable for multi-strategy, then which strategies should be selected in the combined scenario? This paper proposes an approach of multiple strategies detection for ontology mapping. The results obtained so far show that multi-strategy detection improves both on precision and recall significantly.
4301104
Discovering the staring people from social networks
In this paper, we study a novel problem of staring people discovery from social networks, which is concerned with finding people who are not only authoritative but also sociable in the social network. We formalize this problem as an optimization programming problem. Taking the co-author network as a case study, we define three objective functions and propose two methods to combine these objective functions. A genetic algorithm based method is further presented to solve this problem. Experimental results show that the proposed solution can effectively find the staring people from social networks.
207773102
Anisotropic Feature-Preserving Smoothing of 3D Mesh
23010555
User Profile Management for Personalized Telecom Service
With the emergence of Information Communication Technology (ICT), enabling end users to take part in telecom service creation is critical for telecom operators. To stimulate the production of end user-generated services, the service platform should provide adaptation and personalization mechanisms to end users. We present a user profile repository approach that takes account of the personalization and adaptation of telecom service during creation and execution. It is shown how to interact with other components in order to enable creation, deployment and execution of services in a personalized way. We design the architecture of user profile repository, and implement a prototype to verify the functionality. We present how to use IETF XCAP (XML Configuration Access Protocol) protocol to handle user profiles data, and use SIP notification mechanism to subscribe user profiles data in the User Profile Repository.
12619937
Content-Based 3D Model Retrieval for Digital Museum
In this paper, we propose a new shape feature for shape-similarity search of 3D polygonal-mesh models in digital museum. The shape feature is an extension of the D2 shape functions proposed by Osada. Our proposed shape feature is a combination of geometry and texture which is also invariant to similarity transformation. Experiments showed that, our method achieved better performance improvement especially for appearance retrieval of 3d model.
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