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...

Description logics and reasoners, which are descendants of the kl-one language, have been studied in depth in Artificial Intelligence. After a brief introduction, we survey in this paper their application to the problems of information management, using the framework of an abstract information server equipped with several operations -- each involving one or more languages. Specifically, we indicate how one can achieve enhanced access to data and knowledge by using descriptions in languages for schema design and integration, queries, answers, updates, rules, and constraints.

Invisibility is an inherent and significant problem in the task of developing large software systems. There are no direct solutions to this problem

Knowledge-base management systems (KBMS) based on description logics are being used in a variety of situations where access is needed to large amounts of data stored in existing relational databases. We present the architecture and algorithms of a system that converts most of the inferences made by the KBMS into a collection of SQL queries, thereby relying on the optimization facilities of existing DBMS to gain e#ciency, while maintaining an object-centered view of the world with a substantive semantics and significantly di#erent reasoning facilities than those provided by Relational DBMS and their deductive extensions. We address a number of optimization issues that arise in the translation process due to the fact that SQL queries with di#erent syntax (but identical semantics) are not treated uniformly by current database management systems.

This paper surveys terminological representation languages and feature-based unification grammars pointing out the similarities and differences between these two families of attributive description formalisms. Emphasis is given to the logical foundations of these formalisms.

We rst explore the similarities and di erences between concept de nitions in description/terminological logics such as KL-ONE, Classic, Back, Loom, etc. and the types normally encountered in programming languages. The

This article outlines an approach that avoids these problems by choosing a retrieval method that utilizes minimal repository structure to effectively support the process of finding software components. The approach is demonstrated through a pair of proof-ofconcept prototypes: PEEL, a tool to semiautomatically identify reusable components, and CodeFinder, a retrieval system that compensates for the lack of explicit knowledge structures through a spreading activation retrieval process. CodeFinder also allows component representations to be modified while users are searching for information. This mechanism adapts to the changing nature of the information in the repository and incrementally improves the repository while people use it. The combination of these techniques holds potential for designing software repositories that minimize up-front costs, effectively support the search process, and evolve with an organization's changing needs.

What is that makes a “good ” logical model of IR? What are the guidelines that we should follow when we want to build one, and how much can we depart from these guidelines and still claim to have a logical model of IR? We have been motivated to write this note from our dissatisfaction with the fact that there seem to be many competing, incompatible views of what a logical model of IR should consist of; we think some of these views are misleading. 1 Information Retrieval and modelling In recent years, researchers in Information Retrieval (IR) have devoted an increasing amount of work to the design of models of IR, i.e. of theoretical descriptions of the IR task that could serve both as specifications for building running systems, and as theoretical tools for abstractly investigating the relative effectiveness of systems built along their guidelines. Modelling is fundamentally an activity of abstraction. A model is a description of a system that concentrates on the most important, architectural features of the system, and leaves out details that are believed not to be

Frame-based knowledge representation and reasoning systems typically provide procedural interfaces for asking about properties of individuals and concepts. We propose an alternative declarative approach that extends standard interface functionality by supporting selective viewing of components of complex objects. Instead of just returning sets of individuals, our queries match concepts and ltered fragments of descriptions. The query language is an extended form of the language used to describe the knowledge-base contents, thus facilitating user training. In this paper, we describe a variety of possible semantics for answering queries in description logics. We investigate the algorithms required when answers are deduced by matching queries against a \structural normal form" of descriptions. As part of our approach, we introduce a useful re nement of the notion of structural subsumption. 1

ABSTRACT This paper will concentrate on enhancements made to the expressiveness of NIKL but will also describe some improvements NIKL (a New Implementation of KL-ONE) is one of the members and additions made to the NIKL environment. An introduction to of the KL-ONE family of knowledge representation languages. NIKL will be included as background material and enhancements NIKL has been in use for several years and our experiences have to the support of NIKL will be mentioned for the sake of led us to define and implement various extensions to the completeness. language, its support environment and the implementation. Our experiences are particular to the use of NIKL. However, the requirements that we have discovered are relevant to any 2. BACKGROUND intelligent system that must reason about terminology. This article KL-ONE was designed by [Brachman 78] to "circumvent reports on the extensions that we have found necessary based on common expressiveness shortcomings. " It was designed to experiences in several different testbeds. The motivations for the embody the principles that concepts are formal representational extensions and future plans are also presented. objects and that epistemological relationships between formal objects must be kept distinct from conceptual relations between the things that the formal objects represent. KL.ONE defined an 1. INTRODUCTION "epistemologically