Xiaoqing Zheng

Towards Semantic e-Science for Traditional Chinese Medicine

BackgroundRecent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science.ResultsWe present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research.ConclusionSemantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. Its possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.

Data extraction from web pages based on structural-semantic entropy

Most of todays web content is designed for human consumption, which makes it difficult for software tools to access them readily. Even web content that is automatically generated from back-end databases is usually presented without the original structural information. In this paper, we present an automated information extraction algorithm that can extract the relevant attribute-value pairs from product descriptions across different sites. A notion, called structural-semantic entropy, is used to locate the data of interest on web pages, which measures the density of occurrence of relevant information on the DOM tree representation of web pages. Our approach is less labor-intensive and insensitive to changes in web-page format. Experimental results on a large number of real-life web page collections are encouraging and confirm the feasibility of the approach, which has been successfully applied to detect false drug advertisements on the web due to its capacity in associating the attributes of records with their respective values.

A computational trust model for semantic web based on bayesian decision theory

Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.

A scalable probabilistic approach to trust evaluation

The Semantic Web will only achieve its full potential when users have trust in its operations and in the quality of services and information provided, so trust is inevitably a high-level and crucial issue. Modeling trust properly and exploring techniques for establishing computational trust is at the heart of the Semantic Web to realize its vision. We propose a scalable probabilistic approach to trust evaluation which combines a variety of sources of information and takes four types of costs (operational, opportunity, service charge and consultant fee) and utility into consider during the process of trust evaluation. Our approach gives trust a strict probabilistic interpretation which can assist users with making better decisions in choosing the appropriate service providers according to their preferences. A formal robust analysis has been made to examine the performance of our method.

A reputation-chain trust model for the semantic Web

Trust plays a central role for efficient and secure interactions on the semantic Web. Nowadays, more and more research has focused on this topic. In this paper, we introduce RCSW, a reputation-chain trust model for the semantic Web. We illustrate the basic definitions and mechanisms of RCSW and present the algorithm to calculate trust ratings through the chain of the trust network. RCSW incorporates pairwise trust values and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust ratings. We also define classes of trust functions to allow users to individualize their trust calculation. Our proposed model can deal with trust management effectively and can quickly adapt to the changing environment.

Developing a composite trust model for multi-agent systems

The Semantic Web, conceived as a collection of agents, brings new opportunities and challenges to trust research. Enabling trust to ensure more effective and efficient interaction is at the heart of the Semantic Web vision. We propose a composite trust model based on statistical decision theory and Bayesian sequential analysis to balance the costs and benefits during the process of trust evaluating and combine a variety of sources of information to assist users with making the correct decisions in selecting the appropriate service providers according to their preferences. The model proposed by this paper gives trust a strict mathematical interpretation in terms of probability theory.

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.

Develop a Computational Trust Prototype for the Semantic Web

Nowadays, more and more research has focused on the trust management of the Semantic Web. Some mature technologies from many fields such as multiagent systems, social network analysis and artificial intelligence have been introduced to this field. However, some unique characteristics of the Semantic Web are more or less ignored. In this paper, we try to explore the semantic aspects of the trust management. We present DartTrust, a trust prototype for the semantic web in the application context of Traditional Chinese Medicine (TCM). We introduce RCSW - a Reputation-Chain trust model for the Semantic Web, which incorporates pairwise trust factor and reliable factor and constructs an edge-weighted graph to calculate trust ratings. We define a small trust ontology to illustrate the trust relationships. The trust ontology is described in OWL that can be understood and processed by machines. Therefore, DartTrust can provide reasoning functions and facilitate trust management with little human effort. As a demonstration of the prototype, we present its implementation on Traditional Chinese Medicine (TCM) at the end of this paper.

Query optimization in database grid

DarGrid II is an implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of databases in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, we present the design of a query optimizer in DartGrid II, and a heuristic, dynamic, and parallel query optimization approach for processing query in database grid is proposed.

A Deep Neural Network for Modeling Music

We propose a convolutional neural network architecture with k-max pooling layer for semantic modeling of music. The aim of a music model is to analyze and represent the semantic content of music for purposes of classification, discovery, or clustering. The k-max pooling layer is used in the network to make it possible to pool the k most active features, capturing the semantic-rich and time-varying information about music. Our network takes an input music as a sequence of audio words, where each audio word is associated with a distributed feature vector that can be fine-tuned by backpropagating errors during the training. The architecture allows us to take advantage of the better trained audio word embeddings and the deep structures to produce more robust music representations. Experiment results with two different music collections show that our neural networks achieved the best accuracy in music genre classification comparing with three state-of-art systems.