User traversals on hyperlinks between Web pages can reveal semantic relationships between these pages. We use user traversals on hyperlinks as weights to measure semantic relationships between Web pages. On the basis of these weights, we propose a novel method to put Web pages on a Web site onto different conceptual levels in a link hierarchy. We develop a clustering algorithm called PageCluster, which clusters conceptually-related pages on each conceptual level of the link hierarchy based on their in-link and out-link similarities. Clusters are then used to construct a conceptual link hierarchy, which is visualized in a prototype called Online Navigation Explorer (ONE) for adaptive Web site navigation. Our experiments show that our method can put Web pages onto conceptual levels of a link hierarchy more accurately than both the breadth-first search method and the shortest-weighted-path method, and PageCluster can cluster conceptually-related pages more accurately than the bibliographic analysis method. Our user study also shows that the conceptual link hierarchy visualized in ONE can help users find information more effectively and efficiently as the task of finding information becomes less specific and involves more Web pages on multiple conceptual levels.

Introduces a frame metadata model to facilitate the continuous association rules of Web transactions. A new set of association rules can be derived with the updating of the Web log file by the Web transactions in the frame metadata model. This model consists of two types of classes: static classes and active classes. The static classes describe the Web transactions of the association rule table. The active classes are event-driven, obtaining Web transactions when invoked by a certain event. Whenever an update occurs in the existing Web transactions in the Web log file, a corresponding update is invoked by an event attribute in the method class which computes the association rules continuously. The result is active Web mining that is capable of deriving association rules of Web transactions continuously or incrementally using the frame metadata model.

Connectionist networks are interesting computational models that have been proved to be useful for a range of applications. Knowledge in connectionist networks is encoded in distributed internal weights. Learning algorithms based on numerical optimisation techniques can adapt these weights for a specific task (e.g., pattern classification). One of the major criticisms against the connectionist approach, however, is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation, i.e., they are often regarded as black boxes. The authors discuss the ability of information exchange between connectionist and symbolic representations in a novel hybrid knowledge-based connectionist network, called HKBCN. With the newly developed hybrid approach, domain knowledge can be encoded into the network, revised over time, and decoded into symbolic forms.

The ideal situation for a Data Mining or Knowledge Discovery system would be for the user to be able to pose a query in the actuarial domain.

Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.

Abstract This paper introduces the database programming language RAPP, which is an integration of the data type relation and relational algebraic operations with the modular multiprocessing language Pascal Plus. A portable version of the RAPP compiler has been implemented and is described.