Computer Science

The Semantic Web initiative has led to an upsurge of the interest in rules as a general and powerful way of processing, combining, and analyzing semantic information. Since several of the technologies underlying rule-based systems are already quite mature, it is important to understand how such systems might perform on the Web scale. Open-RuleBench is a suite of benchmarks for analyzing the performance and scalability of different rule engines. Currently the study spans five different technologies and twelve systems, but OpenRuleBench is an open community resource, and contributions from the community are welcome. In this report, we describe the tested systems and technologies, the methodology used in testing, and analyze the results.

Development of troubleshooting software is an attractive area of research for agent based system developers. In this project, we attempt to use ontologies extracted from different textual resources to automatically construct a troubleshooting virtual expert. In our solution, we verify the information about the structure of the system extracted from the textual document, then generate a conversation with the user in order to identify the problem and recommend appropriate remedies. To illustrate the approach, we have built knowledge base for a simple use case. A special parser generates conversations that can help the user solve software configuration problems.

Rulelog is a knowledge representation and reasoning language based on logic programming under the well-founded semantics. It is an extension of the language of Flora-2 and so supports inheritance and other object-oriented features, as well as the higher-order syntax of Hilog. However, Rulelog rules may also contain quantifiers and may be contra-positional. In addition, these rules are evaluated in the presence of defeasibility mechanisms that include rule cancellation, rule priorities, and other aspects. Rulelog programs are sometimes developed by loosely coordinated teams of knowledge engineers (KEs) who are not necessarily programmers. This requires not only declarative debugging support, but also support for profiling to help KEs understand the overall structure of a computation, including its termination properties. The design of debugging and profiling tools is made more challenging because Rulelog programs undergo a series of transformations into normal programs, so that there is a cognitive distance between how rules are specified and how they are executed. In this paper, we describe the debugging and profiling environment for Rulelog implemented in the integrated development environment of the Silk system. Our approach includes an interface to justification graphs, which treat why-not and defeasibility as well as provenance of the rules supporting answers. It also includes tools for trace-based analysis of computations to permit understanding of erroneous non-termination and of general performance issues. For semantically correct cases of the nonterminating behavior, Silk offers a different approach, which addresses the problem in a formally sound manner by leveraging a form of bounded rationality called restraint.

Transaction Logic is a logic for representing declarative and procedural knowledge in logic programming, databases, and AI. It has been successful in areas as diverse as workflows and Web services, security policies, AI planning, reasoning about actions, and more. Although a number of implementations of Transaction Logic exist, none is logically complete due to the inherent difficulty and time/space complexity of such implementations. In this paper we attack this problem by first introducing a logically complete tabling evaluation strategy for Transaction Logic and then describing a series of optimizations, which make this algorithm practical. In support of our arguments, we present a performance evaluation study of six different implementations of this algorithm, each successively adopting our optimizations. The study suggest that the tabling algorithm can scale well both in time and space. We also discuss ideas that could improve the performance further.

The Semantic Web initiative has led to an upsurge of the interest in rules as a general and powerful way of processing, combining, and analyzing semantic information. Since several of the technologies underlying rule-based systems are already quite mature, it is important to understand how such systems might perform on the Web scale. OpenRuleBench is a suite of benchmarks for analyzing the performance and scalability of different rule engines. Currently the study spans five different technologies and eleven systems, but