This paper presents the goal, accomplishments and research issues of an NSF project. The project aims to develop a distributed event-triggered knowledge sharing network (ETKnet) for government organizations to share, not only data and application operations, but also knowledge embedded in organizational and inter-organizational policies, regulations, data and security constraints as well as collaborative processes and operating procedures. A unified knowledge and process specification language has been developed to formally specify multi-faceted human and organizational knowledge in terms of three types of knowledge rules and rule structures. A user-friendly interface is provided for collaborating organizations to define events of interest as well as application operations, knowledge rules, rule structures and triggers. Events are published in a global registry for browsing, querying, event subscription and notification. Rules and rule structures are automatically translated into Web services for distributed processing in ETKnet. Event data are dots that can be connected dynamically across organizational boundaries through the interoperation of knowledge rules, processes and application operations.

Conceptual clustering techniques based on current theories of categorization provide a way to design database schemas that more accurately represent classes. An approach is presented in which classes are treated as complex clusters of concepts rather than as simple predicates. An important service provided by the database is determining whether a particular instance is a member of a class. A conceptual clustering algorithm based on theories of categorization aids in building classes by grouping related instances and developing class descriptions. The resulting database schema addresses a number of properties of categories, including default values and prototypes, analogical reasoning, exception handling, and family resemblance. Class cohesion results from trying to resolve conflicts between building generalized class descriptions and accommodating members of the class that deviate from these descriptions. This is achieved by combining techniques from machine learning, specifically explanation-based learning and case-based reasoning. A subsumption function is used to compare two class descriptions. A realization function is used to determine whether an instance meets an existing class description. A new function, INTERSECT, is introduced to compare the similarity of two instances. INTERSECT is used in defining an exception condition. Exception handling results in schema modification. This approach is applied to the database problems of schema integration, schema generation, query processing, and view creation.>

Knowledge mining is the process of discovering knowledge that is hitherto unknown. An approach to knowledge mining by imprecise querying that utilizes conceptual clustering techniques is presented. The query processor has both a deductive and an inductive component. The deductive component finds precise matches in the traditional sense, and the inductive component identifies ways in which imprecise matches may be considered similar. Ranking on similarity is done by using the database taxonomy, by which similar instances become members of the same class. Relative similarity is determined by depth in the taxonomy. The conceptual clustering algorithm, its use in query processing, and an example are presented.>

The use of classification and subsumption to process database queries is discussed. The data model, called CANDIDE, is essentially an extended version of the FL-1, KANDOR and BACK, frame-based knowledge representation languages. A novel feature of the approach is that the data-description language and data-manipulation language are identical, thus providing uniform treatment of data objects, query objects and view objects. The classification algorithm find the correct placement for a query object in a given object taxonomy. Tractability issues are explored, and the expressiveness of queries is compared with relational algebra. This data model has been implemented in POPLOG as the basis for a knowledge-base management system that includes an integrated natural-language query system.>