Zhiming Cui

A Multistrategy Semantic Web Service Matching Approach

Semantic Web makes the automatic discovery and invocation of Web services become possible. But existing methods perform the capability matching, which is crucial for service discovery, either only according to inputs and outputs (IO), which results in a not very precise matching, or trying to match the precondition and effect (PE) of services by FOL, which results in an undecidable reasoning. This paper propose a multistrategy semantic web service matching approach which matchmakes the web service from four aspects such as input/outputs precondition/effect , non-functional description information and quality of service. We will give a formal description of our approach and evaluate it with experiments which demonstrate that our solution yields high-quality results under realistic situation.

Ontology-based focused crawling of deep web sources

Deep Web sources discovery is one of the critical steps toward the large-scale information integration. In this paper, we present Deep Web sources crawling based on ontology, an enhanced crawling methodology. This focused crawling method based on ontology of Deep Web sources avoids to download a large number of irrelevant pages. Evaluation showed that this new approach has promising results.

From Wrapping to Knowledge: Domain Ontology Learning from Deep Web

The next generation of the Web, called Semantic Web, has to improve the Web with semantic page annotations to enable knowledge-level querying and searches. However, manual construction of these ontologies is a time consuming and difficult task. In this paper, we describe an automatic extraction method that learns domain ontologies for semantic web from deep web. Our approach first learns a base ontology from deep web query interfaces, then grows the current ontology by probing the sources and discovering additional concepts and instances from the result pages. We have evaluated our approach in several real-world domains. Preliminary results indicate that the proposed extraction method discovers concepts and instances with high accuracy.

Study of Logistics Vehicle Routing Problem Based on GIS

VRP(vehicle routing problem) is the core of logistics distribution and logistics distribution is also a kind of spatial activities. GIS is an expert to process spatial database. In order to reduce the cost of logistics distribution, to combine the VRP model to GIS by analyzing the VRP model. First to apply spatial clustering to divide customer nodes into several parts according to region density of customer nodes, then to apply Ant Colony Algorithm to optimize the routing in each part. It improves the efficiency of logistics distribution and get a better solution of VRP.

Data Source Selection for Large-Scale Deep Web Data Integration

Deep web has been an important resource on the web due to its rich and high quality information, leading to emerging a new application area in data mining and integrates. There may be hundreds or thousands of data sources providing data of relevance to a particular domain on the web, So a primary challenge to large-scale deep web data integration is to determine in what order to user integrate candidate data sources. In this paper, we develop a most-benefit approach (MBA) for ordering candidate data sources for user integration. At the core of this approach is a utility function that quantifies the utility of a given the state of integration system; thus, we devise a utility function for integration system based on query result number. We show in practice how to efficiently apply MBA in concert with this utility function to order data sources. A detailed experimental evaluation on real datasets shows that the ordering of data sources produced by this MBA-based yields a integration system with a significantly higher utility than a wide range of other ordering strategies.

Quality-based data source selection for web-scale Deep Web data integration

Deep Web has been an important resource on the web due to its rich and high quality information, leading to emerging a new application area in data mining and information retrieval and integrates. In webscale Deep Web data integration tasks, where there may be hundreds or thousands of data sources providing data of relevance to a particular domain, It must be inefficient to integrate all available Deep Web sources. This paper proposes a data source selection approach based on the quality of Deep Web source. It is used for automatic finding the highest quality set of Deep Web sources related to a particular domain, which is a premise for effective Deep Web data integration. The quality of data sources are assessed by evaluating quality dimensions represent the characteristics of Deep Web source. Experiments running on real Deep Web sources collected from the internet show that our provides an effective and scalable solution for selecting data sources for Deep Web data integration.

A Method of Ontology Mapping Based on Instance

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. This paper proposes an ontology similarity calculation method; this method constructs virtual instances for every concept node and takes the structure of ontology into consideration. We apply this method to PROMPT algorithm for ontology mapping and get a higher accuracy and recall through experiment.

Active transfer learning of matching query results across multiple sources

Entity resolution (ER) is the problem of identifying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under supervised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Although such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sampling strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classifiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our experimental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewer labeled samples for record matching with numerous and varied sources.

A Method of Ontology Mapping Based on Subtree Kernel

In semantic web, ontology mapping is the basis of the interoperation of heterogeneous ontologies. Ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. The current ontology mapping methods mainly make the contribution of adjacent nodes into consideration but merely use the characteristic of substructure. This paper proposes a method of ontology mapping based on subtree kernel which uses the influence of substructure to nodes similarity and adjusts the similarity with hyponymy or hypernymy relation of WordNet; We evaluate our approach with experiments which demonstrate that our solution yields a higher accuracy and recall.

A Hybrid Object Matching Method for Deep Web Information Integration

Object matching is a crucial step to integration of Deep Web sources. Existing methods suppose that record extraction and attribute segmentation are of high accuracy. But because of limitation of extraction techniques, information gained through the above methods is often incomplete. If we match object base on noisy and incomplete information, we can not achieve satisfactory performance. This paper proposes a hybrid object matching method, which considers structured and unstructured features and multi-level errors in extraction. We compare performance of the unstructured, structured and hybrid object matching models in our prototype system, which indicates that hybrid method has the highest performance.