Description Logic in Practice: A classic Application

entally evolving knowledge base; and 5. handle errors in a way that keeps the knowledge base consistent, but also provides useful information to the user. We will provide some examples in our domain that illustrate each of these areas. Class and object descriptions: As in any application, we need a domain ontology in which to work. Our home theater application contains a knowledge base including a concept taxonomy and instance descriptions. 1 DACS IV--2000 is a digital cross--connect system that processes digitized signals for some US standard transmission rates. 2 Classic is freely available for academic purposes, and commercially available for other purposes. It has been distributed to over 85 universities and is in use in many internal projects within AT&T. The knowledge base was created by working with an expert in the domain. The database of instance information was also hand--compiled for this small application; how

CLASSIC: A Structural Data Model for Objects

CLASSIC is a data model that encourages the description ofobjects not only in terms of their relations to other known objects, but in terms of a level of intensional structure as well. The CLASSIC language of structured descriptions permits i) partial descriptions of individuals, under an `open world' assumption, ii) answers to queries either as extensional lists of valuesorasdescriptions that necessarily hold of all possible answers, and iii) an easily extensible schema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of defining and populating the DB, as well as querying and answering. classic (for which we have a prototype main-memory implementation) can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB upda..

The CLASSIC Knowledge Representation System or, KL-ONE: The Next Generation

classic is a recently developed knowledge representation (KR) system, based on a view of frames as structured descriptions, with several important inferable relationships, including description classification. While much about classic is novel and important in its own right, it is especially interesting to consider the system in light of its unusual (for Artificial Intelligence) intellectual history: it is the result of over a decade of research and evolution in representation systems that trace their origins back to work on kl-one, arguably one of the most long-lived and influential approaches to KR in the history of AI. We outline some of the novel contributions of classic, but pay special attention to its roots, illustrating the maturation of some of the original features of kl-one, and the decline and fall of others. A number of key ideas are analyzed---including the interpretation of frames as descriptions, the classification inference, and the role of a knowledge representation s..

Integrated Support for Data Archaeology

Corporate databases are potentially rich sources of new and valuable knowledge. Various approaches to "discovering " or "mining " such knowledge have been proposed. We identify an important and previously ignored discovery task, data archaeology. Data archaeology is a skilled human task, in which the knowledge sought depends on the goals of the analyst, cannot be specified in advance, and emerges only through an iterative process of data segmentation and analysis. We describe a system that supports the data archaeologist with a natural, objectoriented representation of an application domain; a powerful query language and database translation routin~s; and an easy-to-use and flexible user interface that supports interactive exploration. A formal knowledge representation system provides the core technology that facilitates database integration, querying, and the reuse of queries and query results. 1