Dietmar Seipel

Unfolding partiality and disjunctions in stable model semantics

This article studies an implementation methodology for partial and disjunctive stable models where partiality and disjunctions are unfolded from a logic program so that an implementation of stable models for normal (disjunction-free) programs can be used as the core inference engine. The unfolding is done in two separate steps. First, it is shown that partial stable models can be captured by total stable models using a simple linear and modular program transformation. Hence, reasoning tasks concerning partial stable models can be solved using an implementation of total stable models. Disjunctive partial stable models have been lacking implementations which now become available as the translation handles also the disjunctive case. Second, it is shown how total stable models of disjunctive programs can be determined by computing stable models for normal programs. Thus an implementation of stable models of normal programs can be used as a core engine for implementing disjunctive programs. The feasibility of the approach is demonstrated by constructing a system for computing stable models of disjunctive programs using the SMODELS system as the core engine. The performance of the resulting system is compared to that of DLV, which is a state-of-the-art system for disjunctive programs.

Clone detection in source code by frequent itemset techniques

In this paper we describe a new approach for the detection of clones in source code, which is inspired by the concept of frequent itemsets from data mining. The source code is represented as an abstract syntax tree in XML. Currently, such XML representations exist for instance for Java, C++, or PROLOG. Our approach is very flexible; it can be configured easily to work with multiple programming languages

Disjunctive Logic Programming: A Survey and Assessment

We describe the fields of disjunctive logic programming and disjunctive deductive databases from the time of their inception to the current time. Contributions with respect to semantics, implementations and applications are surveyed.In the last decade many semantics have been proposed out of which we highlight what we believe to be the most influential ones and compare them. Basic ideas have been borrowed from the semantics of normal logic programs such as stable model semantics and well-founded semantics, which have been generalized in various ways to obtain semantics of disjunctive logic programs.We discuss disjunctive systems such as DLV and Smodels, and related non-disjunctive systems such as XSB and DeReS, that have been implemented. We also describe applications of disjunctive logic programming: reasoning about declarative specifications, reasoning about actions, diagnosis (e.g. in medicine or biology), and in data integration that have resource predicates defined by multiple rules.We discuss the future needs to make the field practical: e.g. integrating concepts from databases (such as aggregation), optimization methods, and object orientation.In Section 12 we discuss the influence that Bob Kowalski had on our work.

Applications of Declarative Programming and Knowledge Management

Invited Talk.- A Guide for Manual Construction of Difference-List Procedures.- Constraints.- Linear Weighted-Task-Sum - Scheduling Prioritized Tasks on a Single Resource.- Efficient Edge-Finding on Unary Resources with Optional Activities.- Encoding of Planning Problems and Their Optimizations in Linear Logic.- Constraint-Based Timetabling System for the German University in Cairo.- Databases and Data Mining.- Squash: A Tool for Analyzing, Tuning and Refactoring Relational Database Applications.- Relational Models for Tabling Logic Programs in a Database.- Integrating XQuery and Logic Programming.- Causal Subgroup Analysis for Detecting Confounding.- Using Declarative Specifications of Domain Knowledge for Descriptive Data Mining.- Extensions of Logic Programming.- Integrating Temporal Annotations in a Modular Logic Language.- Visual Generalized Rule Programming Model for Prolog with Hybrid Operators.- The Kiel Curry System KiCS.- Narrowing for First Order Functional Logic Programs with Call-Time Choice Semantics.- Java Type Unification with Wildcards.- System Demonstrations.- Testing Relativised Uniform Equivalence under Answer-Set Projection in the System cc???.- spock: A Debugging Support Tool for Logic Programs under the Answer-Set Semantics.

Model generation and state generation for disjunctive logic programs

Abstract This paper investigates two fixpoint approaches for minimal model reasoning with disjunctive logic programs P. The first one, called model generation , is based on an operator T P INT defined on sets of Herbrand interpretations whose least fixpoint is logically equivalent to the set of minimal Herbrand models of the program. The second approach, called state generation , uses a fixpoint operation T P s based on hyperresolution . It operates on disjunctive Herbrand states , and its least fixpoint is the set of logical consequences of P, the so-called minimal model state of the program. We establish a useful relationship between hyperresolution by P P s and model generation by T P INT . Then we investigate the problem of continuity of the two operators T P s and T P INT . It is known that the operator T P s is continuous, and so it reaches its least fixpoint in at most ω iterations. On the other hand, the question of whether T P INT is continuous has been open. We show by a counterexample that T P INT is not continuous. Nevertheless, we prove that it converges towards its least fixpoint in at most ω iterations, too, as follows from the relationship that we show exists between hyperresolution and model generation. We define an iterative version of T P INT that computes the perfect model semantics of stratified disjunctive logic programs. On each stratum of the program, this operator converges in at most ω iterations. Model generations for the stable semantics and the partial stable semantics are respectively achieved by using this iterative operator together with the evidential transformation and the 3-S transformation .

Anomalies in ontologies with rules

For the development of practical semantic applications, ontologies are commonly used with rule extensions. Prominent examples of semantic applications not only are Semantic Wikis, Semantic Desktops, but also advanced Web Services and agents. The application of rules increases the expressiveness of the underlying knowledge in many ways. Likewise, the integration not only creates new challenges for the design process of such ontologies, but also existing evaluation methods have to cope with the extension of ontologies by rules. Since the verification of Owl ontologies with rule extensions is not tractable in general, we propose to verify ontologies at the symbolic level by using a declarative approach: With the new language Datalog^@?, known anomalies can be easily specified and tested in a compact manner. We introduce supplements to existing verification techniques to support the design of ontologies with rule enhancements, and we focus on the detection of anomalies that especially occur due to the combined use of rules and ontological definitions.

Incremental development of diagnostic set-covering models with therapy effects

Although a lot of work in the field of knowledge acquisition has been done, the manual development of diagnostic knowledge systems by domain experts still is a very complex task. In this paper we will present an incremental approach for building diagnostic systems based on set-covering models. We start with a simple model describing the coarse structure between diagnoses and findings. Subsequently, this simple model can be enhanced by similarities, weights and probabilities to increase the accuracy of the knowledge and the resulting system. We will also show how these static set-covering models can be combined with dynamic set-covering models including higher level knowledge about causation effects. We will motivate how dynamic set-covering models can be used for implementing diagnostic systems including therapy effects. Finally, we report on two practical applications dealing with set-covering models from the geoecological and from the medical domain, respectively, that we have implemented.

Verification and refactoring of ontologies with rules

Currently, the introduction of an appropriate rule representation layer for the semantic web stack is discussed. However, with the inclusion of rule-based knowledge new verification issues for rule-augmented ontologies arise. In this paper we investigate the detection of anomalies as an important subtask of verification. We extend and revise existing approaches for the syntactic verification of ontologies with respect to the existence of rules, and we introduce new anomalies considering the understandability and maintainability of such ontologies.

Refactoring Methods for Knowledge Bases

The manual development of large knowledge systems is a difficult and error-prone task. In order to facilitate extensions to an existing knowledge base the structural design of the implemented knowledge needs to be improved from time to time. However, experts are often deterred even from important design improvements since some restructurings are too complex to handle. In this paper, we introduce a framework that allows for automated refactorings. Refactoring methods are well-defined and are executed in a semi-automated way. In this manner, the developer is supported during the process of restructuring of even large knowledge bases. Refactoring methods are usually applied to improve the design of the knowledge base; in this paper, we sketch some design anomalies that identify poor design of the knowledge base.

Tracking Topic Evolution in News Environments

For companies acting on a global scale, the necessity to monitor and analyze news channels and consumer-generated media on the Web, such as weblogs and n news-groups, is steadily increasing. In particular the identification of novel trends and upcoming issues, as well as their dynamic evolution over time, is of utter importance to corporate communications and market analysts. Automated machine learning systems using clustering techniques have only partially succeeded in addressing these newly arising requirements, failing in their endeavor to properly assign short-term hype topics to long-term trends. We propose an approach which allows to monitor news wire on different levels of temporal granularity, extracting key-phrases that reflect short-term topics as well as longer-term trends by means of statistical language modelling. Moreover, our approach allows for assigning those windows of smaller scope to those of longer intervals.