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Iterative Refinement of Knowledge Bases with Consistency Guarantees
Abstract
Natural kinds, such as the concepts toy block or photosynthesis, are ubiquitous in human reasoning yet lack definitional (i.e., individually necessary and jointly sufficient) properties as membership conditions. We want to represent a wide variety of assertions about these natural concepts with precision and logical consistency. Furthermore, we want to be able to verify that the meaning of each concept does not conflict with the constraints implied by its constituents. Classification-based approaches to these tasks have relied on definitional properties in order to deduce subsumption and thus detect inconsistency. These approaches are inapplicable for building representations of natural concepts. Our solution to these problems is twofold. First, we build KBs by iterative refinement; i.e., the KB is constructed through a sequence of editing operations. Second, we define practical, yet formal, properties of concept satisfiability and inheritance that each operator is guaranteed to prese...
... Finally, methods for ensuring and maintaining consistency of components are needed, especially as a knowledge base evolves over time. We have recently started research on this issue (Correl & Porter 1997). ...
Our goal is to build knowledge-based systems capable of answering a wide variety of questions, including questions that are unanticipated when the knowledge base is built. For systems to achieve this level of competence and generality, they require the ability to dynamically construct new concept representations, and to do so in response to the questions and tasks posed to them. Our approach to meeting this requirement is to build knowledge bases of generalized, representational components, and to develop methods for automatically composing components on demand. This work extends the normal inheritance approach used in frame-based systems, and imports ideas from several different areas of AI, in particular compositional modeling, terminological reasoning, and ontological engineering. The contribution of this work is a novel integration of these methods that improves the efficiency of building knowledge bases and the robustness of using them. Introduction Our goal is the construction o...
It is currently thought in the knowledge-based systems (KBS) domain that sophisticated tools are necessary for helping an expert with the difficult task of knowledge acquisition. The problem of detecting inconsistencies is especially crucial. The risk of inconsistencies increases with the size of the knowledge base; for large knowledge bases, detecting inconsistencies “by hand” or even by a superficial survey of the knowledge base is impossible. Indeed, most inconsistencies are due to the interaction between several rules via often deep deductions. In this paper, we first state the problem and define our approach in the framework of classical logic. We then describe a complete method to prove the consistency (or the inconsistency) of knowledge bases that we have implemented in the COVADIS system.
Pour le développement de systèmes experts, des outils sophistiqueés d'aide à la constitution de bases de connaissances s'avèrent tout à fait nécessaires. Le probléme de la détection d'incohérences dans la base de connaissances est en particulier crucial. En effet, plus la base de connaissances est importante, plus le risque d'incohérences est grand et difficile à détecter «à la main» ou měme par un simple examen de surface des règles; les causes principales d'incohérences proviennent la plupart du temps de l'interaction de plusieurs règles via de nombreuses déductions. Dans un premier temps, nous posons le problème et définissons notre approche dans le cadre formel de la logique classique. Puis nous proposons une méthode complète pour prouver la cohérence (ou l'incohérence) d'une base de connaissances que nous avons mise en oeuvre dans le système COVADIS.
Levesque and Brachman argue that in order to provide timely and correct responses in the most critical applications, general-purpose knowledge representation systems should restrict their languages by omitting constructs which require nonpolynomial worst-case response times for sound and complete classification. They also separate terminological and assertional knowledge, and restrict classification to purely terminological information. We demonstrate that restricting the terminological language and classifier in these ways limits these “general-purpose” facilities so severely that they are no longer generally applicable. We argue that logical soundness, completeness, and worst-case complexity are inadequate measures for evaluating the utility of representation services, and that this evaluation should employ the broader notions of utility and rationality found in decision theory. We suggest that general-purpose representation services should provide fully expressive languages, classification over relevant contingent information, “approximate” forms of classification involving defaults, and rational management of inference tools.
KL-ONE is a system for representing knowledge in Artificial Intelligence programs. It has been developed and refined over a long period and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community. Here we present the kernel ideas of KL-ONE, emphasizing its ability to form complex structured descriptions. In addition to detailing all of KL-ONE's description-forming structures, we discuss a bit of the philosophy underlying the system, highlight notions of taxonomy and classification that are central to it, and include an extended example of the use of KL-ONE and its classifier in a recognition task.This research was supported in part by the Defense Advanced Research Projects Agency under Contract N00014-77-C-0378. Views and conclusions contained in this paper are the authors' and should not be interpreted as representing the official opinion or policy of DARPA, the U.S. Government, or any person or agency connected with them.
Role-limiting approaches support knowledge acquisition (KA) by centering knowledge base construction on common types of tasks or domain-independent problem-solving strategies. Within a particular problem-solving strategy, domain-dependent knowledge plays specific roles. A KA tool then helps a user to fill these roles. Although role-limiting approaches are useful for guiding KA, they are limited because they only support users in filling knowledge roles that have been built in by the designers of the KA system. EXPECT takes a different approach to KA by representing problem-solving knowledge explicitly, and deriving from the current knowledge base the knowledge gaps that must be resolved by the user during KA. This paper contrasts role-limiting approaches and EXPECT's approach, using the propose-and-revise strategy as an example. EXPECT not only supports users in filling knowledge roles, but also provides support in making other modifications to the knowledge base, including adapting the problem-solving strategy. EXPECT's guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. This work provides evidence supporting the need for explicit representations in building knowledge-based systems.
This paper analyzes the correctness of the subsumption algorithm used in CLASSIC, a description logic-based knowledge representation system that is being used in practical applications. In order to deal efficiently with individuals in CLASSIC descriptions, the developers have had to use an algorithm that is incomplete with respect to the standard, model-theoretic semantics for description logics. We provide a variant semantics for descriptions with respect to which the current implementation is complete, and which can be independently motivated. The soundness and completeness of the polynomial-time subsumption algorithm is established using description graphs, which are an abstracted version of the implementation structures used in CLASSIC, and are of independent interest. Comment: See http://www.jair.org/ for any accompanying files
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