Research On Granular Rough Theory And Key Issues In Computational Web Intelligence

With the rapid development of the Internet, a great number of business applications have been deployed on the Web, which drastically change the way of serving customers to their requirements. However, the huge volume of the data with high uncertainty has been a major problem, which makes it hard to support knowledge discovery and decision-making. It has been a bottleneck in further developing e-Business and other application domains. Aiming to deal with uncertainty on the Web, Computational Web Intelligence (CWI) provides promising methodology to solve above problems.The complex information structures require the infrastructure to supply with a more straightforward information representation method, which incorporates built-in Computational Web intelligence mechanism. To enrich theoretic foundation of the CWI framework from both Granular Computing and Rough Computing perspectives, Granular Rough Theory is proposed to capture the representative semantics of roughness, to accommodate semi-structured information sources, and to be based on pure mereological relations. In application area, modifications based on rater maturity are proposed to improve the Collaborative Filtering algorithms.Related efforts result in following major innovative achievements:(1) The representative semantics of roughness is clarified as an essential feature that makes roughness methodology independent of other soft computing approaches. It is proposed to adjust the underlying representation model of classic Rough Set Theory, in order to explicitly encode semantic contexts underlying schema of original information tables. In the new representation model design, it is taken into account to widen the natural applicability scope of roughness methodology by accommodating semi-structured data with tuples of the "attribute-value" form. And mereological relations are used to describe the structural relations in the new representation model, due to their rich application semantic contexts. It is pointed out that the motivation to build a pure mereological approach to roughness lies in the expectation not only to make use of the close relationship among mereology, spatial informatics and ontology, but also to exhibit potential powers of mereology in the case of building interdisciplinary methodologies.(2) A new representation model called Granular Representation Calculus (GrRC) is presented. In GrRC, the primitive notion is the triple form atomic granule, encapsulating the minimal complete semantic unit of information system. Compound granules are aggregated from atomic ones, and then compose more complex structures with aggregation and fusion operations. Since GrRC plays the role of common representation model for both ordinary information sources and roughness methodology, some special kinds of compound granules with dedicated operations are defined, and mechanism for mereological relation identification is also discussed, in order to support roughness formation. Then roughness formation approach based on GrRC is proposed. By performing aspect shift over conditional granules to decisional attribute, the results are wrapped with decisional granules to identify the reciprocal part to whole relations, due to which, the conditional granules are classified into regular, irregular and irrelevant granules with respect to a given decisional granule. All regular granules aggregate into the kernel granule, standing for the lower approximation in roughness. On the other hand, the irregular ones form the boundary notion in roughness, named as hull granule. Aggregation of both the kernel and the hull granule results in the upper approximation of roughness, called corpus granule. Distinction between Granular Rough Theory and Rough Mereology is clarified for their different point of view in the case of incorporating mereology.(3) Tentative solutions are presented, to adapt Granular Rough Theory to ontological computing and multi-agent contexts. Based on design considerations in major upper level ontology, ontologically applicable concepts in GrRC are explicated or added to, in terms of space, time, quantity category, quality category, modal category and relation category in Kant's framework of synthetic a priori. In multi-agent systems, representation for its particular information system is defined. The notion of information cube is used to visualize special compound granules, and to analyze the way of roughness formation in multi-agent systems. Epistemic collision among agents is explored with definitions of two auxiliary measurements to alleviate the problem. The paradigm of Collaborative Filtering is discussed as a special kind of multi-agent decisional information system, and the underlying information beneath differences in amount of agent's knowledge is suggested as a crucial source for improving quality of Collaborative Filtering algorithms.(4) Hypothesis of Rational Authorities Bias (H-RAB) is proposed to capture the expectation that higher prediction accuracy can be attained by emphasizing more mature referential users. Modifications based on H-RAB are designed in two aspects: RAB-WS (Weight Scaling) is a fine tuning method by scaling original similarity weights with rater's maturity measure, so as to increase influence of more mature referential users; RAB-DR (Data Reduction) is a more audacious one that suggests pruning all referential users with less maturity measure than a given Maturity Threshold. On three major public available Collaborative Filtering datasets, experimental results from a series of experiments empirically justify the soundness of both RAB-aware modifications and validity of H-RAB.(5) Two prototypes of implementing Granular Rough Theory are presented in the sense of data representation and object-oriented programming, respectively. Data representation oriented implementation utilizes open source clinical information system, which is based on Entity-Attribute-Value model. Object-oriented programming implementation is developed with Java classes and objects to incarnate the notion of information granules. Moreover, functional aspects of ontology-driven Web information system framework are illustrated, as a Rough, Granular Web intelligence infrastructure for upper applications.