The huge number of autonomousand heterogeneous data repositories accessible on the “global information infrastructure” makes it impossible for users to be aware of the locations, structure/organization, query languages and semantics of the data in various repositories. There is a critical need to complement current browsing, navigationaland informationretrieval techniques with a strategy that focuses on information content and semantics. In any strategy that focuses on information content, the most critical problem is that of different vocabularies used to describe similar information across domains. We discuss a scalable approach for vocabulary sharing. The objects in the repositories are represented as intensional descriptions by pre-existing ontologies expressed in Description Logics characterizing information in different domains. User queries are rewritten by using interontologyrelationships to obtain semanticspreserving translations across the ontologies. 1.

Inamultidatabase system, schematic con icts between two objects are usually of interest only when the objects have some semantic similarity. We use the concept of semantic proximity, which is essentially an abstraction/mapping between the domains of the two objects associated with the context of comparison. An explicit though partial context representation is proposed and the speci city relationship between contexts is de ned. The contexts are organized as a meet semi-lattice and associated operations like the greatest lower bound (glb) are de ned. The context of comparison and the type of abstractions used to relate the two objects form the basis of a semantic taxonomy. Atthesemantic level, the intensional description of database objects provided by the context is expressed in a description logic language. Schema correspondences are used to store mappings from the semantic level to the data level and are associated with the respective contexts. Inferences about database content at the federation level are modeled as changes in the context and the associated schema correspondences. We try to reconcile the dual (schematic and semantic) perspecitves by: enumerating possible semantic similarities between objects having schema and data conicts, and modeling schema correspondences as the projection of semantic proximity wrt context. 1

Computational tractability has been a major concern in the area of terminological knowledge representation and reasoning. However, all analyses of the computational complexity of terminological reasoning are based on the hidden assumption that subsumption in terminologies reduces to subsumption of concept descriptions without a significant increase in computational complexity. In this paper it will be shown that this assumption, which seems to work in the "normal case," is nevertheless wrong. Subsumption in terminologies turns out to be co-NP-complete for a minimal terminological representation language that is a subset of every useful terminological language.

This paper describes the design of a pattern matcher for a knowledge representation system called LOOM. The pattern matcher has a very rich pattern-forming language, and is logic-based, with a deductive mechanism which includes a truth-maintenance component as an integral part of the pattern-matching logic. The technology behind the LOOM matcher uses an inference engine called a classifier to perform the matches. The LOOM matcher is more expressive and more complete than previous classi cationbased pattern-matchers, and is expected to be significantly more efficient.

Terminological systems, such as KL-ONE and K-Rep, are widely used in AI to represent and reason with concept descriptions. They compute subsumption relations between concepts and automatically classify concepts into a taxonomy. Each concept in the taxonomy describes a set of possible instances which are a superset of those described by its descendants. One limitation of current systems is their inability to handle complex compositions of concepts, such as constraint networks where each node is described by an associated concept. For example, plans are often represented (in part) as collections of actions related by a rich variety of temporal constraints. The T-REX system integrates terminological reasoning with constraint network reasoning to classify such plans, producing a "terminological" plan library. T-REX also introduces a new view of plan recognition as a process which dynamically partitions the plan library by modalities, e.g., necessary, possible and impo...

In the past 20 years, AI researchers in knowledge representation (KR) have implemented over 50 frame knowledge representation systems (FRSs). KR researchers have explored a large space of alternative FRS designs. This paper surveys the FRS design space in search of design principles for FRSs. The FRS design space is defined by the set of alternative features and capabilities --- such as the representational constructs --- that an FRS designer might choose to include in a particular FRS, as well as the alternative implementations that might exist for a particular feature. The paper surveys the architectural variations explored by different system designers for the frame, the slot, the knowledge base, for accessoriented programming, and for object-oriented programming. We find that few design principles exist to guide an FRS designer as to how particular design decisions will affect qualities of the resulting FRS, such as its worst-case and average-case theoretical complexity, its actual...

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...

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This paper provides a model theoretic semantics to feature terms augmented with set descriptions. We provide constraints to specify HPSG style set descriptions, fixed cardinality set descriptions, set-membership constraints, restricted universal role quantifications, set ration, intersection, subset and disjointhess. A sound, complete and terminating consistency checking procedure is provided to determine the consistency of any given term in the logic. It is shown that determining consistency of terms is a NP-complete problem. Subject Areas: feature logic, constraint-based gram- mars, HPSG 1

The Frame-logic (F-logic) approach of [20] is suggested as an underlying framework for description languages. F-logic is shown to provide a full account for description languages, without losing the direct semantics and the descriptive nature. It can support such desirable features as high order role fillers, collective entities, intensions, roles as first class objects and n-ary relationships. Yet, its semantics is first order. In an F-logic based description language, few description constructs are built in, and concepts, roles, and terminological operators are definable. Discussion of desirable features in descriptions is made possible within a single, uniform framework, that also coherently integrates with logic programming and deductive, object-oriented database technology. Typical descriptive operators can be defined in the language, thereby yielding a flexible description language, in which not all operators must be built in. keywords: object-oriented representation, F-logic, de...