Weiyi Liu

Discovering semantic associations among Web services based on the qualitative probabilistic network

In recent years, the intelligent management and decision of Web services have attracted more and more attention due to the wide applications in various aspects of the real world. With the increase of Web services in an organization, the desired on-line services should be located rapidly requiring not only the syntactic but also the semantic techniques. In addition, aiming at fulfilling complex applications by discovering and composing available services automatically and precisely, it is indispensable to develop an underlying model and the corresponding measure for semantic associations among given Web services. In this paper, by mining the historical invocations of component services, we first construct a semantic model to describe their behavior rules based on the qualitative probabilistic network. Further, we propose a distance measure and the approach to discovering semantic associations among Web services. Preliminary experiments and performance studies show that our methods are feasible. Moreover, high recall and precision can be achieved when our methods are applied to Web service search.

Constructing the Bayesian network structure from dependencies implied in multiple relational schemas

Research highlights? We propose the method for constructing Bayesian network structures from multiple 3NF relational schemas. ? We establish our method based on the acyclic database theory and its relationship with probabilistic networks. ? The Bayesian network structure is constructed from relational schemas instead of database instances. Relational models are the most common representation of structured data, and acyclic database theory is important in relational databases. In this paper, we propose the method for constructing the Bayesian network structure from dependencies implied in multiple relational schemas. Based on the acyclic database theory and its relationships with probabilistic networks, we are to construct the Bayesian network structure starting from implied independence information instead of mining database instances. We first give the method to find the maximum harmoniousness subset for the multi-valued dependencies on an acyclic schema, and thus the most information of conditional independencies can be retained. Further, aiming at multi-relational environments, we discuss the properties of join graphs of multiple 3NF database schemas, and thus the dependencies between separate relational schemas can be obtained. In addition, on the given cyclic join dependency, the transformation from cyclic to acyclic database schemas is proposed by virtue of finding a minimal acyclic augmentation. An applied example shows that our proposed methods are feasible.

Qualitative probabilistic networks with reduced ambiguities

A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.

Semantic integration of XML Schema

The availability of large amounts of heterogeneous distributed Web data necessitates the integration of XML data from multiple XML sources. As a common method for defining and validating highly structured XML documents, XML Schema is considered a logical model for XML. This paper describes how to convert XML Schema to UML diagram (conceptual model) for semantic integration and how to integrate conceptual models. Our integration process includes three steps: clustering of concepts, unification of concepts, and restructuring of relationships. Finally, a global conceptual model is provided for users.

Qualitative probabilistic networks with rough-set-based weights

A qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network by encoding variables and the qualitative influences between them in a directed acyclic graph. In order to provide for measuring the weights of qualitative influences and resolving trade-offs during inferences, in this paper we introduce rough-set-based weights to the qualitative influences of QPNs. Looking upon each variable as an equivalence relation on the given sample data table, we give the method to obtain the weights based on the concept of dependency degree in the rough set theory, and learn the enhanced QPN with weighted influences, called EQPN. Then we discuss the conflict-free EQPN inferences and give the method to resolve trade-offs by addressing the symmetry, transitivity and composition properties.

Naïve Bayesian Classification of Uncertain Objects Based on the Theory of Interval Probability

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naive Bayesian classifier from uncertain objects. So the paper studies Naive Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naive Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances.

A dynamic algorithm for community detection in social networks

Social networks can be modeled by graphs with nodes and edges, and communities are sub graphs within networks. This paper proposes a new dynamic algorithm based on the modularity given by Newman and Girvan (NG modularity for short). Further more, this paper applies the proposed algorithm to real network data. The experimental results show that our algorithm can dynamically detect communities in a network, and the communities detected with the algorithm fits better with the real communities.

Autonomous discovery of subgoals using acyclic state trajectories

Divide and rule is an effective strategy to solve large and complex problems. We propose an approach to make agent can discover autonomously subgoals for task decomposition to accelerate reinforcement learning. We remove the state loops in the state trajectories to get the shortest distance of every state from the goal state, then these states in acyclic state trajectories are arranged in different layers according to the shortest distance of them from the goal state. So, to reach these state layers with different distance to the goal state can be used as the subgoals for agent reaching the goal state eventually. Compared with others, autonomy and robustness are the major advantages of our approach. The experiments on Grid-World problem show the applicability, effectiveness and robustness of our approach.

A State-Cluster Based Q-Learning

When apply Q-learning to complex real-world problems, the learning process is long enough to make this method unpractical. The major cause is Q-learning requires the agent to visit every state-action transition infinitely often for making Q value convergent. We propose a State-Cluster based Q-learning method to accelerate convergence and shorten learning process. This method creates the State-Cluster for each state the agent reached according to the state trajectory that the agent wandered. By our algorithm, the State-Cluster of a state would hold these acyclic shortest state paths from other states to this state. When a states Q value is refined in one step of the agent, the refined Q value can be propagated immediately back to all these states in its State-Cluster along the state paths between them, instead of requiring the agent to visit these states again. With the State-Cluster, more Q value can be refined in one step of the agent, which speeds up the convergence of Q value. The experiments compared with Q-learning demonstrate this method is extraordinarily more effective. This method is aimed Q-learning, but it is also applicable for most other reinforcement learning methods based value function iteration.

A game method for multiple attribute decision-making without weight information

A game model for solving multiple attribute decision making without weight information is proposed. In this model, each attribute is a player whose strategy is to choose a value to endow its attribute weight, and whose utility is matching degree between alternative order produced by decision making and alternative order produced by magnitude of attribute. The Nash equilibrium solution is obtained by using Genetic Algorithm, and an equilibrium profile is an attribute weight vector. Under the equilibrium profile??the matching degree between alternative order produced by decision making and all alternative orders produced by magnitude of attribute values are better. The results of experiments show that the game method is effective and feasible.