Toni Bollinger

The LILOG knowledge representation system

The LILOG knowledge representation system is part of LEU/2 - the LILOG Experimentier Umgebung1 - a natural language understanding system for German. The knowledge representation system comprises a sophisticated knowledge representation system comprises a sophisticated knowledge representation language based on order-sorted predicate logic enriched by a type system of KL-ONE like languages, default reasoning, and the capability to delegate inferences to external deductive components. The inference engine processing LLILOG can be considered as an experimental theorem proving shell since, for example, we are able to exchange inference calculi and search strategies very easily. We sketch the knowledge representation language LLILOG, give an overview of the internal architecture of the LILOG inference engine, and show how the inference engine is embedded into the natural language understanding system LEU/2.

Grid-based Data Mining in Real-life Business Scenario

This paper presents a Grid-based distributed and parallel data mining system targeting a real-life application scenario typical in the business realm - franchise supermarket basket analysis. Following a layered design of three tiers, this system enables parallel association rule mining on a farm of Grid servers, offers a standard service interface for custom applications, and provides a friendly user portal. The work presented in this paper reveals specific requirements for applying Grid-based data mining in the business realm, which is helpful for the design and implementation of a generic Grid-based data mining system.

Knowledge in operation

The L, , knowledge representation language and an inference engine to interpret it have been developed as part of the LILOG project, where now concepts for understanding natural-language texts were investigated. L, is a typed predicate logic whose type system has adopted the concepts of KL-One-like languages. Further language constructs allow the formulation of default and control knowledge. The inference engine for L, was designed as an experimental theorem prover, allowing us to investigate the behavior of various inference calculi as well as a number of search strategies. Processing with LLILoG is not restricted to a propositional reasoner for logical formulas; we are also able to delegate special kinds of inferences to external deductive components. Currently, one such external reasoner for processing spatial information on the basis of analog representation is attached to the inference engine.

Künstliche Intelligenz und Umweltanwendungen

Erfahrungen aus Kooperationsprojekten der letzten Zeit haben gezeigt, das der interdisziplinare Dialog zwischen der KI und der Okologie2 fur beide Gebiete fruchtbar sein kann. So hat sich im EXCEPT-Projekt gezeigt, das durch die wissensbasierte Modellierung von okologischen Fragestellungen die Theoriebildung innerhalb der Okologie vorangetrieben werden kann. Der Zwang zur Formalisierung last Lucken und Redundanzen in bisher eher informell beschrieben Verfahren erkennen. Auserdem bieten Informatik und KI Methoden und Konzepte an, durch die okologische Sachverhalte beschrieben und operationalisiert werden konnen. Die Informatik dient in diesem Zusammenhang also als Strukturwissenschaft.

Knowledge Processing in the LILOG Project: From the first to the second Prototype

This paper gives an overview of the knowledge processing in the LILOG project. We describe the knowledge processing component as it has been realized within the first prototype and give some experimental results. The experiences gained with the first prototype motivated us to improve the knowledge processing for the second prototype. These modifications are presented in the second part of this paper.