The technology for building large knowledge bases (KBs) is yet to witness a breakthrough so that a KB can be constructed by the assembly of prefabricated knowledge components. Knowledge components include both pieces of domain knowledge (for example, theories of economics or fault diagnosis) and KB tools (for example, editors and theorem provers). Most of the current KB development tools can only manipulate knowledge residing in the knowledge representation system (KRS) for which the tools were originally developed. Open Knowledge Base Connectivity (OKBC) is an application programming interface for accessing KRSs, and was developed to enable the construction of reusable KB tools. OKBC improves upon its predecessor, the Generic Frame Protocol (GFP), in several signi cant ways. OKBC can be used with a much larger range of systems because its knowledge model supports an assertional view of a KRS. OKBC provides an explicit treatment ofentities that are not frames, and it has a much better way of controlling inference and specifying default values. OKBC can be used on practically any platform because it supports network transparency and has implementations for multiple programming languages. In this paper, we discuss technical design issues faced in the development of OKBC, highlight how OKBC improves upon GFP, and report on practical experiences in using it.

Activities such as Web Services and the Semantic Web are working to create a distributed web of machine understandable data. We address three important problems that need to be solved to realize this vision. We discuss the problem of scalable and deployable query systems and present a simple, but general query interface called GetData. We address the issue of creating global agreements on vocabularies and introduce the concept of Semantic Negotiation, a process by which two programs can bootstrap from small shared vocabularies to larger shared vocabularies. We discuss the problem of programs determining which data sources to trust and present a solution that uses a Web of Trust between Semantic Web registries. We briefly describe TAP, a system that implements the GetData interface, Semantic Negotiation and Web of Trust enabled registries. We then introduce an application of the Semantic Web called Semantic Search and describe an implemented system which uses the data from the Semantic Web to improve traditional search results.

This paper is a report on the first phase of a long-term, interdisciplinary project whose goal is to increase the overall effectiveness of physicians ’ time, and thus the quality of health care, by improving the information exchange between physicians and patients in clinical settings. We are focusing on patients with long-term and chronic conditions, initially on migraine patients, who require periodic interaction with their physicians for effective management of their condition. We are using medical informatics to focus on the information needs of patients, as well as of physicians, and to address problems of information exchange. This requires understanding patients’ concerns to design an appropriate system, and using state-of-the-art artificial intelligence techniques to build an interactive explanation system. In contrast to many other knowledge-based systems, our system’s design is based on empirical data on actual information needs. We used ethnographic techniques to observe explanations actually given in clinic settings, and to conduct interviews with migraine sufferers and physicians. Our system has an extensive knowledge base that contains both general medical terminology and specific knowledge about migraine, such as

It has long been noted that Natural Language utterances can communicate more than their conventional meaning (Grice, 1975). It has also been noted that behaving appropriately in response to instructions given in Natural Language requires understanding more than their conventional meaning (Suppes and Crangle, 1988; Webber and Di Eugenio, 1990; Webber et al., 1992). This paper addresses one mechanism by which speakers convey, and hearers derive, such additional aspects of meaning -- a mechanism we call pragmatic overloading. In pragmatic overloading, a clause interpreted as conveying directly or indirectly the goal fi of an action ff which is described by some other clause, forms the basis of constrained inference that leads to additional information about the action ff. The paper demonstrates pragmatic overloading through a variety of clausal adjuncts. We then discuss a framework that supports many of the inferences that pragmatic overloading gives rise to. This framework integrates a ...

. Grasper-CL is a system for manipulating and displaying graphs, and for building graph-based user interfaces for application programs. It is implemented in COMMON LISP and CLIM, and has been proven by use in a number of applications. Grasper-CL includes several advances in graph drawing. It contains a graph abstract datatype plus a comprehensive and novel language of operations on that datatype. The appearance of Grasper-CL graphs can be tailored by a wide variety of shape parameters that allow the application to customize the display of nodes and edges for different domains. Default values for shape parameters can be established at several levels. Grasper-CL employs a toolbox approach to graph layout: the system contains a suite of graph layout algorithms that can be applied individually, or in combination to produce hierarchical graph layouts. The system also contains an interactive graph browser. Keywords: Graphs, Graph drawing, Graph layout 1. Introduction Graphs are virtually ubi...

In this work, we are interested in how rational decision makers reason with and about reasons in a domain, practical ethics, where they appear to reason about reasons symbolically in terms of both abstract moral principles and case comparisons. The challenge for reasoners, human and artificial, is to use abstract knowledge of reasons and principles to inform decisions about the salience of similarities and differences among cases while still accounting for a case's or problem's specific contextual circumstances. TRUTH-TELLER is a program we have developed and tested that compares pairs of cases presenting ethical dilemmas about whether to tell the truth. The program's methods for reasoning about reasons help it to make context sensitive assessments of the salience of similarities and differences.

Most of today's terminological representation systems implement hybrid reasoning architectures wherein a concept classifier is employed to reason about concept definitions, and a separate recognizer is invoked to compute instantiation relations between concepts and instances. Whereas most of the existing recognizer algorithms designed to maximally exploit the reasoning supplied by the concept classifier, Loom has experimented with recognition strategies that place less emphasis on the classifier, and rely more on the abilities of Loom's backward chaining query facility. This paper presents the results of experiments that test the performance of the Loom algorithms. These results suggest that, at least for some applications, the Loom approach to recognition is likely to outperform the classical approach. They also indicate that for some applications, much better performance can be achieved by eliminating the recognizer entirely, in favor of a purely backward chaining architecture. We conclude that no single recognition algorithm or strategy is best for all applications, and that an architecture that offers a choice of inference modes is likely to be more useful than one that offers only a single style of reasoning.

Large volumes of genomic sequences are being produced at increasing rates for many living organisms. The computer analysis of these data, using numerical and symbolic methods, generates various and numerous objects (such as genes, signals, repetitive sequences, etc.), the status of which is often hypothetical. Object-based knowledge models allow to describe these objects which are inter-related and sometimes organised in higher level structures, such as operons in the case of bacterial genomes. Annotations under the form of hypertext can be associated to these descriptions. Methodological knowledge on the analysis methods themselves can also be described. These capabilities are presented on the example of two operational object-oriented knowledge bases which have been designed in the context of a tight collabora - tion with the "Laboratoire de Biométrie, Génétique et Biologie des Populations" of Claude Bernard University in Lyon. It is argued that such knowledge bases can become a very...

We informally introduce TaxLog, a close integration of logic programming and terminological reasoning. Terminological systems are handling declarative logic-based descriptions of conceptual knowledge. Most of them restrict their expressiveness and focus on (efficient) reasoning algorithms for certain services. This imposes principal restrictions on the expressivity of such a formalism. To get maximal benefit of terminological reasoning, while being able to overcome these expressiveness deficiencies, the terminological system Taxon has been integrated with logic programming by applying a CLP scheme to its assertional formalism. Because Taxon itself is an amalgamation of an abstract concept language by concrete domains (such as predicates over rational numbers) we have a three-layered system architecture: 1) rules on the basis of a tuned vocabulary which is formulated in 2) a concept language that is grounded by 3) concrete domains. We will discuss this layered architecture as a main pre...

This paper gives algorithms to compute the explanation of instance membership in classes over an Inference Hierarchy. O#c# time algorithms are given to compute the positive, negative, and conditional explanations over an Inference Hierarchy limited to conjunctive parents, where c is the total number of conditions in the Inference Hierarchy 1 . Algorithms requiring O#c 2 k# time and space are given for computing the k-minimal positive, negative and conditional explanations over disjunctive hierarchies with independent conditions. Worst-case exponential time is required for computing negative and negative conditional explanations over disjunctive hierarchies with dependent conditions. All types of explanation are worst-case exponential time for the k-minimal explanations over hierarchies using negation in parent relationships. An application for these algorithms is provided. The application is motivated by the DARPA ALP storyboard and provides active mediation of airport facility status in a combat situation. These techniques may be transferred to financial, administrative, and environmental domains as well. 1